Introduction
This notebook contains analyses from the MEDAL study, assessing
memory bias in remitted and depressed individuals. The pre-registration
for the main analyses, as well as a preprint version of the accompanying
article can be found here as well.
The main aim of the current work was to examine how three groups
(control, remitted, depressed) differ in terms of emotional memory
dynamics in a real-life setting.
#Load packages
Warning message:
In Sys.timezone() : unable to identify current timezone 'C':
please set environment variable 'TZ'
#renv::activate()
library(dplyr)
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library(janitor)
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library(readxl)
library(data.table)
data.table 1.14.8 using 4 threads (see ?getDTthreads). Latest news: r-datatable.com
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library(ggplot2)
Learn more about the underlying theory at https://ggplot2-book.org/
library(psych)
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library(devtools)
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library(pastecs)
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library(cli)
library(lmerTest) #linear mixed models
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library(performance) #fits of residuals
library(DescTools)
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print.palette wesanderson
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library(tidyr) # Separate trigger column
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library(sjPlot) # for nice tables and interaction plots
library(knitr) # to print the tables within notebook
library(foreach) #run parallel loops
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library(parallel)
library(doParallel) #run parallel loops
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library(ggpubr) # emmeans
library(mediation) #mediation analysis
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mediation: Causal Mediation Analysis
Version: 4.5.0
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library(plotly) #for interactive plots
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library(jtools) #for theme_apa
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library(JSmediation) #moderated mediation
library(wesanderson)
source("functions.R")
Data
Here we load in our data and preprocess it in steps. We first need to
separate the different surveys that were delivered to bring them into
the same sampling times, merge in some descriptives we need as
covariates, and estimate subject-centered and lagged variables for our
models later.
- Load and clean data
# Load the data
MEDAL_ESM_compleet <- read_excel("data/MEDAL_ESM_compleet.xlsx")
# Move variables around and clean names
MEDAL_ESM_compleet= MEDAL_ESM_compleet %>%
rename(id='Movisens-ID') %>%
clean_names() %>%
rename(subjectcode = 'deelnemersnr') %>%
relocate('subjectcode', .after = 'id')
df_medal_try = MEDAL_ESM_compleet
- Split different EMA surveys we have
# Sleep EMA Qs
df_medal_sleep = df_medal_try %>%
filter(form=='Slaap') %>%
dplyr::select(c(1, 2, 4, 5, 7, 9, 14))
# Recent EMA Qs
df_medal_recent = df_medal_try %>%
filter(form=='Recent') %>%
dplyr::select(c(1, 2, 4, 5, 7, 9, contains('rec')))
# Remote EMA Qs
df_medal_remote = df_medal_try %>%
filter(form=='Remote') %>%
dplyr::select(c(1, 2, 4, 5, 7, 9, contains('rem')))
# Mastery EMA Qs
df_medal_mastery = df_medal_try %>%
filter(form=='Mastery') %>%
dplyr::select(c(1, 2, 4, 5, 7, 9, contains('master')))
# Standard Qs
df_medal_standard = df_medal_try %>%
filter(form=='Standaard') %>%
dplyr::select(c(1, 2, 4, 5, 7, 9, (-contains('mastery') & -contains('rem') & -contains('rec')))) %>%
dplyr::select(-c(7,13,14))
# merge sleep and standard
df_medal_merged = merge(df_medal_sleep, df_medal_standard, by=c('id', 'trigger_counter', 'subjectcode'), suffixes = c("_sleep", ""), all=T )
# merge to recent, remote and mastery
df_medal_merged= merge(df_medal_merged, df_medal_recent, by=c('id', 'trigger_counter', 'subjectcode' ), suffixes = c('', '_recent'), all = T )
df_medal_merged = merge(df_medal_merged, df_medal_remote, by=c('id', 'trigger_counter', 'subjectcode' ), suffixes = c('', '_remote'), all = T )
df_medal_merged = merge(df_medal_merged, df_medal_mastery, by=c('id', 'trigger_counter', 'subjectcode' ), suffixes = c('', '_mastery'), all = T )
# Fix start dates and times
df_medal_merged$form_start_date <- as.character(df_medal_merged$form_start_date)
df_medal_merged$form_start_date <- as.ITime(df_medal_merged$form_start_date)
df_medal_merged = df_medal_merged %>%
dplyr::mutate(form_start_date = ifelse(is.na(form_start_date), form_start_date_sleep, form_start_date))
- Add variables on Education, Gender, and Age
# Load in previous data for demographics (also contains EMA)
load("data/IPAQ_Maeve_Workspace.RData")
df_MEDAL <- df_MEDAL[-c(192), ]
# Select relevant demo info
df_MEDAL = df_MEDAL %>%
dplyr::select(1, 3, 5:7) %>%
rename('subjectcode' = 'subject', 'education' = 'Education3groups', 'n_episodes'='NumberEpisodes' )
# merge demo to full EMA
df_medal_merged = merge(df_medal_merged, df_MEDAL, by = c('subjectcode'), all = T ) %>%
clean_names()
df_medal_merged$education = as.factor(df_medal_merged$education)
df_medal_merged$gender = as.factor(df_medal_merged$gender)
df_medal_merged = df_medal_merged %>%
relocate(c('id', 'gender', 'age', 'education'), .after = 'subjectcode') %>%
relocate ('age', .after = 'gender') %>%
relocate ('education', .after = 'age') %>%
relocate ('id', .before = 'subjectcode')
- Categorize the subjectcode into the three conditions (Depressed,
Remitted, Control (Never-depressed))
df_medal_merged$condition = case_when(df_medal_merged$subjectcode >= 700 & df_medal_merged$subjectcode < 800 ~ "remitted", df_medal_merged$subjectcode >= 800 & df_medal_merged$subjectcode < 900 ~ "depressed", df_medal_merged$subjectcode >= 900 ~ "control")
df_medal_merged = df_medal_merged %>% relocate ('condition', .after = 'subjectcode' )
df_medal_merged$condition <- as.factor(df_medal_merged$condition)
- Calculate reaction time
df_medal_merged$rt_sleep = lubridate::as.difftime( df_medal_merged$form_finish_date_sleep - df_medal_merged$form_start_date_sleep)
- Separate time from when trigger was sent
df_medal_merged = df_medal_merged %>%
tidyr::separate(trigger, c('Random', 'Time', 'Trigger_Time_1', 'Trigger_Time_2')) %>%
dplyr::mutate(trigger_time = paste(Trigger_Time_1, Trigger_Time_2, sep=':')) %>%#separate time from previous trigger
dplyr::mutate(trigger = paste(Random, Time, trigger_time, sep=' ')) #bring back 'trigger' column
#Turn numbers into time format
df_medal_merged$trigger_time <- as.ITime(df_medal_merged$trigger_time)
- Find the trigger number per day
#df_medal_merged$trigger_counter[is.na(df_medal_merged$trigger_time)] <- 1
df_medal_merged$trigger_number[is.na(df_medal_merged$trigger_time)] <- 1
df_medal_merged$trigger_number[df_medal_merged$trigger_time >= (as.ITime("10:00:00"))& df_medal_merged$trigger_time < (as.ITime("12:00:00"))] <- 2
df_medal_merged$trigger_number[df_medal_merged$trigger_time >= (as.ITime("12:00:00"))& df_medal_merged$trigger_time < (as.ITime("14:00:00"))] <- 3
df_medal_merged$trigger_number[df_medal_merged$trigger_time >= (as.ITime("14:00:00"))& df_medal_merged$trigger_time < (as.ITime("16:00:00"))] <- 4
df_medal_merged$trigger_number[df_medal_merged$trigger_time >= (as.ITime("16:00:00"))& df_medal_merged$trigger_time < (as.ITime("18:00:00"))] <- 5
df_medal_merged$trigger_number[df_medal_merged$trigger_time >= (as.ITime("18:00:00"))& df_medal_merged$trigger_time < (as.ITime("20:00:00"))] <- 6
df_medal_merged$trigger_number[df_medal_merged$trigger_time >= (as.ITime("20:00:00"))& df_medal_merged$trigger_time < (as.ITime("23:00:01"))] <- 7
# df
df_medal_merged = df_medal_merged %>%
relocate ('trigger_number', .after = 'trigger_counter')%>% relocate('trigger_time', .before ='form_start_date') %>%
relocate('trigger', .before ='trigger_time')
- Final cleaning of the dataset
#Create dataset where double morning list is removed
df_medal_merged = df_medal_merged %>%
dplyr::mutate(trigger_counter=ifelse(trigger_number==1 & lag(trigger_number)==1, lag(trigger_counter), trigger_counter)) %>%
dplyr::mutate(trigger_counter=ifelse(trigger_number==1 & lag(trigger_number)==1, lag(trigger_counter), trigger_counter)) %>%
dplyr::mutate(trigger_counter=ifelse(trigger_number==1 & lag(trigger_number)==1, lag(trigger_counter), trigger_counter)) %>%
dplyr::mutate(trigger_counter=ifelse(trigger_number==1 & lag(trigger_number)==1, lag(trigger_counter), trigger_counter)) %>%
dplyr::mutate(trigger_counter=ifelse(trigger_number==1 & lag(trigger_number)==1, lag(trigger_counter), trigger_counter))
# now we have the clean dataframe, remove duplicated coloumns
df_medal_clean = df_medal_merged %>%
dplyr::group_by(subjectcode) %>%
distinct(trigger_counter, .keep_all = TRUE) %>%
dplyr::select (1:12, 17:60) %>%
dplyr::mutate(original_order = row_number()) %>%
relocate('original_order', .before = 'id')
#Set the order of the factor to control, remission, depression (for visual purposes)
df_medal_clean$condition <- factor(df_medal_clean$condition, levels=c('control', 'remitted', 'depressed' ), labels=c('control', 'remitted', 'depressed'))
# Create dummy variable
df_medal_clean = df_medal_clean %>%
mutate(condition_dummy = case_when(condition == 'depressed'~ 3, condition == 'remitted'~ 2, condition == 'control' ~ 1))
df_medal_clean$condition_dummy = as.factor(df_medal_clean$condition_dummy)
#set weekday
df_medal_clean$form_finish_date <- as.character(df_medal_clean$form_finish_date)
df_medal_clean$form_finish_date <- strptime(df_medal_clean$form_finish_date, format = '%Y-%m-%d %H:%M:%S')
df_medal_clean$finish_date <- as.Date(df_medal_clean$form_finish_date)
df_medal_clean$weekday <- as.integer(format(df_medal_clean$finish_date, '%w'))
df_medal_clean = df_medal_clean %>% relocate('weekday', .after = 'trigger_number')
#solve duplicate issue
df_medal_duplicate = df_medal_clean %>% dplyr::group_by(subjectcode, weekday) %>% filter(duplicated(trigger_number))
#since row 10 for subject 712 is a duplicate morning list
df_medal_clean <- subset(df_medal_clean, !(subjectcode == 712 & original_order == 10))
df_medal_clean = df_medal_clean %>% dplyr::group_by(subjectcode, weekday) %>%
mutate(temp = lead(trigger_number),
trigger_number = case_when(trigger_number == temp ~ (temp -1), TRUE ~ trigger_number)) %>%
select(-temp)
#Take the rows out that have 'NA'for the weekday since form wasn't finished and there is no data for PA, NA, or Memory
df_medal_clean = df_medal_clean %>% filter(!is.na(weekday))
df_medal_clean$original_order <- 1:nrow(df_medal_clean)
# Create expanded df with all potential datapoints
df_medal_day = df_medal_clean %>%
dplyr::select('subjectcode', 'weekday', 'original_order') %>%
dplyr::mutate(weekday = as.numeric(weekday)) %>%
dplyr::group_by(subjectcode) %>%
dplyr:: distinct(weekday, .keep_all =T) %>%
dplyr::ungroup()
df_medal_day = df_medal_day %>%
slice(rep(1:n(), each = 7)) %>%
dplyr::mutate(trigger_number = rep(1:7, length.out=n())) %>%
dplyr::rename(order = original_order)
#merge with main dataframe
df_medal_clean = merge(df_medal_day, df_medal_clean, by=c('subjectcode', 'trigger_number', 'weekday'), all= T)
df_medal_clean = df_medal_clean %>% mutate(order = as.numeric(order))
df_medal_clean = arrange(df_medal_clean, order)
- Centre, scale, and average Variables
used_vars = c("neg_mood", "rec_mem_neg_mood", "rem_mem_neg_mood", "pos_mood", "rec_mem_pos_mood", "rem_mem_pos_mood")
#Centering & scaling
df_medal_clean = df_medal_clean %>%
dplyr::group_by(subjectcode) %>%
# Center and scale
dplyr::mutate( across(used_vars, ~ (.x/10), .names = "{.col}" ),
across(used_vars, ~ mean(.x, na.rm=T), .names = "{.col}_m" ),
across(used_vars, ~ (.x - mean(.x, na.rm=T)), .names = "{.col}_c" ),
across(paste0(used_vars, "_c"), ~ DescTools::Winsorize(.x, na.rm = T), .names = "{.col}")) %>%
ungroup() %>%
# Rescale to positive
mutate(across(c(paste0(used_vars,"_c")), ~ abs(min(.x, na.rm = T)) + 1 + .x, .names = "{.col}s"))
- Lag variables
# Remote
df_medal_clean = df_medal_clean %>%
dplyr::group_by(subjectcode) %>%
mutate(rem_mem_pos_mood_lag = lead(rem_mem_pos_mood, n=7, order_by=subjectcode),
rem_mem_neg_mood_lag = lead(rem_mem_neg_mood, n=7, order_by=subjectcode),
pos_mood_lag_rem = lead(pos_mood, n=7, order_by=subjectcode),
neg_mood_lag_rem = lead(neg_mood, n=7, order_by=subjectcode),
rem_mem_pos_mood_cs_lag = lead(rem_mem_pos_mood_cs, n=7, order_by=subjectcode),
rem_mem_neg_mood_cs_lag = lead(rem_mem_neg_mood_cs, n=7, order_by=subjectcode),
pos_mood_cs_lag_rem = lead(pos_mood_cs, n=7, order_by=subjectcode),
neg_mood_cs_lag_rem = lead(neg_mood_cs, n=7, order_by=subjectcode),
rem_mem_pos_mood_c_lag = lead(rem_mem_pos_mood_c, n=7, order_by=subjectcode),
rem_mem_neg_mood_c_lag = lead(rem_mem_neg_mood_c, n=7, order_by=subjectcode),
pos_mood_c_lag_rem = lead(pos_mood_c, n=7, order_by=subjectcode),
neg_mood_c_lag_rem = lead(neg_mood_c, n=7, order_by=subjectcode)) %>%
filter(!is.na(original_order)) # filter out the NA rows for the lagging of recent memory
# Recent
df_medal_clean = df_medal_clean %>%
dplyr::group_by(subjectcode) %>%
mutate(rec_mem_pos_mood_lag = lead(rec_mem_pos_mood, n=1, order_by=subjectcode),
rec_mem_neg_mood_lag = lead(rec_mem_neg_mood, n=1, order_by=subjectcode),
pos_mood_lag_rec = lead(pos_mood, n=1, order_by=subjectcode),
neg_mood_lag_rec = lead(neg_mood, n=1, order_by=subjectcode),
rec_mem_pos_mood_cs_lag = lead(rec_mem_pos_mood_cs, n=1, order_by=subjectcode),
rec_mem_neg_mood_cs_lag = lead(rec_mem_neg_mood_cs, n=1, order_by=subjectcode),
pos_mood_cs_lag_rec = lead(pos_mood_cs, n=1, order_by=subjectcode),
neg_mood_cs_lag_rec = lead(neg_mood_cs, n=1, order_by=subjectcode),
rec_mem_pos_mood_c_lag = lead(rec_mem_pos_mood_c, n=1, order_by=subjectcode),
rec_mem_neg_mood_c_lag = lead(rec_mem_neg_mood_c, n=1, order_by=subjectcode),
pos_mood_c_lag_rec = lead(pos_mood_c, n=1, order_by=subjectcode),
neg_mood_c_lag_rec = lead(neg_mood_c, n=1, order_by=subjectcode)) %>%
ungroup()
Descriptives
We first plot some general population descriptives, followed by some
descriptives of the scales we use in the EMA weeks and compliance
rates.
Population Descriptives
#Create Descriptives Tables
summary_table = df_medal_clean %>% distinct(subjectcode, .keep_all = TRUE) %>% group_by(condition) %>% summarise(
'N' = n(),
'Age \n M +/- SD' = paste0(round(mean(age, na.rm =T), 1), ' +/- ' , round(sd(age, na.rm =T), 1)),
#'NA Age' = sum(is.na(age)),
'% Education Low' = mean(education == 0, na.rm = T)*100,
'% Education Middle' = mean(education == 1, na.rm = T)*100,
'% Education High' = mean(education == 2, na.rm = T)*100,
#'NA Education' = sum(is.na(education)),
'% Female' = mean(gender ==1, na.rm = T)*100,
#'NA Gender' = sum(is.na(gender))
)
#Compliance rates
df_medal_compliance_individual = df_medal_clean %>% group_by(condition, subjectcode) %>% summarise('ComplianceRate' = sum(!is.na(trigger_number))/42*100)
`summarise()` has grouped output by 'condition'. You can override using the `.groups` argument.
length(which(df_medal_compliance_individual$ComplianceRate <70))
[1] 8
df_medal_compliance = df_medal_clean %>% group_by(condition) %>% summarise('% Mean Compliance' = sum(!is.na(trigger_number))/(n_distinct(subjectcode) *42)*100)
# Create a table
summary_table = summary_table %>% left_join(df_medal_compliance, by = 'condition')
rempsyc::nice_table(summary_table)
condition | N | Age
M +/- SD | % Education Low | % Education Middle | % Education High | % Female | % Mean Compliance |
|---|
control | 55 | 51.7 +/- 10.9 | 0.00 | 22.64 | 77.36 | 78.18 | 92.42 |
remitted | 90 | 52.5 +/- 11.1 | 3.49 | 16.28 | 80.23 | 78.41 | 91.88 |
depressed | 46 | 48.7 +/- 11.2 | 4.35 | 30.43 | 65.22 | 71.74 | 92.65 |
#create dataset with only unique subjectcode
df_medal_distinct = df_medal_clean %>% distinct(subjectcode, .keep_all = TRUE)%>% select('subjectcode', 'condition', 'age', 'education', 'gender')
#Test for differences between groups
#Age
df_medal_distinct %>% lm(age ~ condition, data=.) %>% summary()
Call:
lm(formula = age ~ condition, data = .)
Residuals:
Min 1Q Median 3Q Max
-26.717 -6.511 2.489 7.489 18.283
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 51.6727 1.4876 34.736 <2e-16 ***
conditionremitted 0.8386 1.8963 0.442 0.659
conditiondepressed -2.9553 2.2042 -1.341 0.182
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 11.03 on 186 degrees of freedom
(2 observations deleted due to missingness)
Multiple R-squared: 0.0192, Adjusted R-squared: 0.008657
F-statistic: 1.821 on 2 and 186 DF, p-value: 0.1648
#education
chisq.test(df_medal_distinct$education, df_medal_distinct$condition)
Warning: Chi-squared approximation may be incorrect
Pearson's Chi-squared test
data: df_medal_distinct$education and df_medal_distinct$condition
X-squared = 5.8234, df = 4, p-value = 0.2127
#gender
chisq.test(df_medal_distinct$gender, df_medal_distinct$condition)
Pearson's Chi-squared test
data: df_medal_distinct$gender and df_medal_distinct$condition
X-squared = 0.84533, df = 2, p-value = 0.6553
#Create a Graph
#age
boxplot_age <- ggplot(df_medal_distinct, aes(x=condition, y=age, fill=condition)) +
geom_boxplot() +
labs(x = 'Condition', y = 'Age', tag = 'C' ) +
ggtitle('Age per Condition') +
scale_fill_manual(values = wesanderson::wes_palette(n=3, name='GrandBudapest2' )) +
theme_apa()
ggplotly(boxplot_age)
Warning: Removed 2 rows containing non-finite values (`stat_boxplot()`).
#education
plot_educ <- ggplot(df_medal_distinct, aes(x=condition, fill = education)) +
geom_bar(position = 'dodge') +
labs(x='Condition', y= 'Proportion', tag = 'A' ) +
ggtitle('Education Level per Condition') +
scale_fill_manual(values = wesanderson::wes_palette(n=3, name='GrandBudapest2' ), labels = c('Low' , 'Middle' , 'High'), name = 'Education') +
theme_apa()
ggplotly(plot_educ)
#Gender
plot_gender <- ggplot(df_medal_distinct, aes(x=condition, fill = gender)) +
geom_bar(position = 'dodge') +
labs(x = 'Condition', y = 'Proportion', tag = 'B' ) +
ggtitle('Gender per Condition') +
scale_fill_manual(values = wesanderson::wes_palette(n=3, name='GrandBudapest2' ), labels = c('Male', 'Female'), 'Gender') +
theme_apa()
ggplotly(plot_gender)
plot_gender_2 <- ggstatsplot::ggbarstats(data = df_medal_distinct, x = gender, y = condition) +
labs(x = 'Condition', y = 'Proportion', tag = 'B' ) +
ggtitle('Gender per Condition') +
scale_fill_manual(values = wesanderson::wes_palette(n=3, name='GrandBudapest2' ), labels = c('Male', 'Female'), 'Gender') +
theme_apa()
Scale for fill is already present.
Adding another scale for fill, which will replace the existing scale.
ggplotly(plot_gender_2)
Warning: geom_GeomLabel() has yet to be implemented in plotly.
If you'd like to see this geom implemented,
Please open an issue with your example code at
https://github.com/ropensci/plotly/issues
ggpubr::ggarrange(plot_educ, plot_gender, boxplot_age, nrow=2, ncol=2)
Warning: Removed 2 rows containing non-finite values (`stat_boxplot()`).
NA
Positive Mood
describe.by(df_medal_clean$pos_mood, group=df_medal_clean$condition) %>% rbindlist() %>% mutate(vars=c("cont", "rem", "dep"))
Warning: describe.by is deprecated. Please use the describeBy function
Distribution
ggplot(data= df_medal_clean, aes(x=pos_mood)) + geom_histogram()+facet_grid(cols=vars(condition))
Centred Distribution
ggplot(data= df_medal_clean, aes(x=pos_mood_c)) + geom_histogram()+facet_grid(cols=vars(condition))
Positive Recent Memory
describe.by(df_medal_clean$rec_mem_pos_mood, group=df_medal_clean$condition) %>% rbindlist() %>% mutate(vars=c("cont", "rem", "dep"))
Warning: describe.by is deprecated. Please use the describeBy function
Distribution
ggplot(data= df_medal_clean, aes(x=rec_mem_pos_mood)) + geom_histogram()+facet_grid(cols=vars(condition))
Centred Distribution
ggplot(data= df_medal_clean, aes(x=rec_mem_pos_mood_c)) + geom_histogram()+facet_grid(cols=vars(condition))
Positive Remote Memory
describe.by(df_medal_clean$rem_mem_pos_mood, group=df_medal_clean$condition) %>% rbindlist() %>% mutate(vars=c("cont", "rem", "dep"))
Warning: describe.by is deprecated. Please use the describeBy function
Distribution
ggplot(data= df_medal_clean, aes(x=rem_mem_pos_mood)) + geom_histogram()+facet_grid(cols=vars(condition))
Centred Distribution
ggplot(data= df_medal_clean, aes(x=rem_mem_pos_mood_c)) + geom_histogram()+facet_grid(cols=vars(condition))
Negative Mood
describe.by(df_medal_clean$neg_mood, group=df_medal_clean$condition)%>% rbindlist() %>% mutate(vars=c("cont", "rem", "dep"))
Warning: describe.by is deprecated. Please use the describeBy function
Distribution
ggplot(data= df_medal_clean, aes(x=neg_mood)) + geom_histogram()+facet_grid(cols=vars(condition))
Centred Distribution
ggplot(data= df_medal_clean, aes(x=neg_mood_c)) + geom_histogram()+facet_grid(cols=vars(condition))
ggplot(data= df_medal_clean, aes(x=neg_mood_cs)) + geom_histogram()+facet_grid(cols=vars(condition))
Negative Recent Memory
describe.by(df_medal_clean$rec_mem_neg_mood, group=df_medal_clean$condition) %>% rbindlist() %>% mutate(vars=c("cont", "rem", "dep"))
Warning: describe.by is deprecated. Please use the describeBy function
Distribution
ggplot(data= df_medal_clean, aes(x=rec_mem_neg_mood)) + geom_histogram()+facet_grid(cols=vars(condition))
Centred Distribution
ggplot(data= df_medal_clean, aes(x=rec_mem_neg_mood_c)) + geom_histogram()+facet_grid(cols=vars(condition))
Negative Remote Memory
describe.by(df_medal_clean$rem_mem_neg_mood, group=df_medal_clean$condition) %>% rbindlist() %>% mutate(vars=c("cont", "rem", "dep"))
Warning: describe.by is deprecated. Please use the describeBy function
Distribution
ggplot(data= df_medal_clean, aes(x=rem_mem_neg_mood)) + geom_histogram()+facet_grid(cols=vars(condition))
Centred Distribution
ggplot(data= df_medal_clean, aes(x=rem_mem_neg_mood_c)) + geom_histogram()+facet_grid(cols=vars(condition))
Main
Effects
A simple linear regression analysis was done to look at the
differences in positive and negative mood ratings per condition.
Linear Mixed Models
for PA and NA
#positive mood
positive_model = lmer(pos_mood ~ condition + (1|subjectcode), data = df_medal_clean)
#negative mood
negative_model = lmer(neg_mood ~ condition + (1|subjectcode), data = df_medal_clean)
asis_output(tab_model (positive_model, negative_model,
show.se = T, show.df = T, show.aic = T, transform = NULL,
show.stat = T, show.std = T,
title = 'Condition Differences Mood Rating', dv.labels = c('PA Scaled', 'NA Scaled') )$knitr)
Condition Differences Mood Rating
|
|
PA Scaled
|
NA Scaled
|
|
Predictors
|
Estimates
|
std. Error
|
std. Beta
|
standardized std. Error
|
CI
|
standardized CI
|
Statistic
|
p
|
df
|
Estimates
|
std. Error
|
std. Beta
|
standardized std. Error
|
CI
|
standardized CI
|
Statistic
|
p
|
df
|
|
(Intercept)
|
7.49
|
0.17
|
0.56
|
0.08
|
7.15 – 7.83
|
0.40 – 0.71
|
43.49
|
<0.001
|
7381.00
|
1.14
|
0.20
|
-0.63
|
0.08
|
0.74 – 1.54
|
-0.79 – -0.47
|
5.63
|
<0.001
|
7377.00
|
|
condition [remitted]
|
-1.12
|
0.22
|
-0.51
|
0.10
|
-1.55 – -0.69
|
-0.71 – -0.32
|
-5.13
|
<0.001
|
7381.00
|
1.43
|
0.26
|
0.58
|
0.10
|
0.93 – 1.93
|
0.38 – 0.78
|
5.57
|
<0.001
|
7377.00
|
|
condition [depressed]
|
-2.90
|
0.26
|
-1.32
|
0.12
|
-3.40 – -2.40
|
-1.55 – -1.10
|
-11.38
|
<0.001
|
7381.00
|
3.68
|
0.30
|
1.49
|
0.12
|
3.10 – 4.27
|
1.26 – 1.73
|
12.29
|
<0.001
|
7377.00
|
|
Random Effects
|
|
σ2
|
2.12
|
2.12
|
|
τ00
|
1.58 subjectcode
|
2.20 subjectcode
|
|
ICC
|
0.43
|
0.51
|
|
N
|
191 subjectcode
|
191 subjectcode
|
|
Observations
|
7386
|
7382
|
|
Marginal R2 / Conditional R2
|
0.233 / 0.560
|
0.295 / 0.653
|
|
AIC
|
27174.561
|
27219.514
|
Plot
#create boxplot for negative and positive affect for each group with significance levels
combine_data = df_medal_clean %>%
tidyr::pivot_longer(cols=c(pos_mood, neg_mood), names_to = 'mood_type', values_to = 'mood_rating') %>%
dplyr::mutate(mood_type = factor(mood_type, levels = c('pos_mood' , 'neg_mood')))
# Plot
lm_plot = ggplot(combine_data, aes(x=condition, y = mood_rating, fill= condition)) +
geom_boxplot() +
labs(x ='Group', y ='Affect (scaled units)' ) +
scale_x_discrete(labels = c('control' = 'Never Depressed', 'remitted' = 'Remitted', 'depressed' = 'Depressed' )) +
facet_wrap( ~ mood_type, ncol =3, nrow = 2, labeller = labeller(mood_type = c('pos_mood' = 'Positive Affect', 'neg_mood' = 'Negative Affect'))) +
geom_signif(comparisons = list(c('control', 'remitted'), c('control' , 'depressed'), c('remitted' , 'depressed' )), map_signif_level = T, y_position = c(15, 13, 11)) +
scale_fill_manual(values = wesanderson::wes_palette(n=3, name='GrandBudapest2' )) +
theme_apa() +
guides(fill=F)
ggplotly(lm_plot)
ggsave('plot_simple_regression.pdf', dpi = 320, width = 12, height = 8, path = "figures/")
Follow-Up
emmeans::emmeans(positive_model, pairwise ~ condition, pbkrtest.limit = 7800, lmerTest.limit=7800)
$emmeans
condition emmean SE df lower.CL upper.CL
control 7.49 0.172 188 7.15 7.83
remitted 6.37 0.135 188 6.10 6.63
depressed 4.59 0.188 187 4.21 4.96
Degrees-of-freedom method: kenward-roger
Confidence level used: 0.95
$contrasts
contrast estimate SE df t.ratio p.value
control - remitted 1.12 0.219 188 5.129 <.0001
control - depressed 2.90 0.255 188 11.381 <.0001
remitted - depressed 1.78 0.232 188 7.696 <.0001
Degrees-of-freedom method: kenward-roger
P value adjustment: tukey method for comparing a family of 3 estimates
emmeans::emmeans(negative_model, pairwise ~ condition, pbkrtest.limit = 7800, lmerTest.limit=7800)
$emmeans
condition emmean SE df lower.CL upper.CL
control 1.14 0.202 188 0.74 1.54
remitted 2.57 0.158 188 2.26 2.88
depressed 4.82 0.221 188 4.39 5.26
Degrees-of-freedom method: kenward-roger
Confidence level used: 0.95
$contrasts
contrast estimate SE df t.ratio p.value
control - remitted -1.43 0.257 188 -5.571 <.0001
control - depressed -3.68 0.300 188 -12.290 <.0001
remitted - depressed -2.25 0.272 188 -8.282 <.0001
Degrees-of-freedom method: kenward-roger
P value adjustment: tukey method for comparing a family of 3 estimates
Exploratory
Analyses
Moderated Mediation
RemPA-CurrPA
df_medal_moderation = df_medal_clean_remote %>%
filter(!(is.na(pos_mood) | is.na(rem_mem_pos_mood_lag)))
#mediation first
med_fit_h2_pos_rem <- lme4::lmer(rem_mem_pos_mood_lag ~ condition_dummy +
age + gender + education +
(1 |subjectcode),
data =df_medal_moderation)
# full model
out_fit_h2_pos_rem <-lme4::lmer(pos_mood ~ condition_dummy + rem_mem_pos_mood_lag
+ age + gender +education +
(1 + rem_mem_pos_mood_lag |subjectcode),
data=df_medal_moderation)
Warning: Model failed to converge with max|grad| = 0.00602257 (tol = 0.002, component 1)
#mediation analysis for control and remitted
med_pos_rem_rem <- mediation::mediate(med_fit_h2_pos_rem, out_fit_h2_pos_rem,
treat = 'condition_dummy' , control.value = 1, treat.value = 2 , mediator = 'rem_mem_pos_mood_lag')
summary(med_pos_rem_rem)
Causal Mediation Analysis
Quasi-Bayesian Confidence Intervals
Mediator Groups: subjectcode
Outcome Groups: subjectcode
Output Based on Overall Averages Across Groups
Estimate 95% CI Lower 95% CI Upper p-value
ACME (control) -0.663 -0.980 -0.36 <2e-16 ***
ACME (treated) -0.663 -0.980 -0.36 <2e-16 ***
ADE (control) -0.369 -0.680 -0.07 0.012 *
ADE (treated) -0.369 -0.680 -0.07 0.012 *
Total Effect -1.032 -1.453 -0.63 <2e-16 ***
Prop. Mediated (control) 0.643 0.420 0.91 <2e-16 ***
Prop. Mediated (treated) 0.643 0.420 0.91 <2e-16 ***
ACME (average) -0.663 -0.980 -0.36 <2e-16 ***
ADE (average) -0.369 -0.680 -0.07 0.012 *
Prop. Mediated (average) 0.643 0.420 0.91 <2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Sample Size Used: 771
Simulations: 1000
#create new dataframes with only control remitted and only control depressed
df_medal_con_rem = df_medal_clean %>% filter(condition=='control' | condition == 'remitted')
df_medal_con_dep = df_medal_clean %>% filter(condition=='control' | condition == 'depressed')
#remitted group
df_medal_con_rem$condition_dummy = build_contrast(df_medal_con_rem$condition, 'control', 'remitted')
med_h2_pos_rem_rem <- mdt_moderated(data = df_medal_con_rem, IV =condition_dummy, DV = pos_mood, M = rem_mem_pos_mood_lag, Mod = pos_mood_lag_rem)
rbindlist(med_h2_pos_rem_rem$paths, idcol = T)
NA
RemNA - CurrNA
df_medal_moderation = df_medal_clean_remote %>% filter(!(is.na(neg_mood) | is.na(rem_mem_neg_mood_lag)))
#mediation first
med_fit_h2_neg_rem <- lme4::lmer(rem_mem_neg_mood_lag ~ condition_dummy +
age + gender + education +
(1 |subjectcode),
control = lmerControl(calc.derivs = F, optimizer='bobyqa', optCtrl=list(maxfun=1e6)),
data =df_medal_moderation)
out_fit_h2_neg_rem <-lme4::lmer(neg_mood ~ condition_dummy + rem_mem_neg_mood_lag +
age + gender + education +
(1 + rem_mem_neg_mood_lag |subjectcode),
control = lmerControl(calc.derivs = F, optimizer='bobyqa', optCtrl=list(maxfun=1e6)),
data=df_medal_moderation)
#mediation analysis for control and depressed
med_neg_rem_dep <- mediation::mediate(med_fit_h2_neg_rem, out_fit_h2_neg_rem,
mediator = 'rem_mem_neg_mood_lag', treat = 'condition_dummy' ,
control.value = 1, treat.value = 3 , sims=10000)
summary(med_neg_rem_dep)
Causal Mediation Analysis
Quasi-Bayesian Confidence Intervals
Mediator Groups: subjectcode
Outcome Groups: subjectcode
Output Based on Overall Averages Across Groups
Estimate 95% CI Lower 95% CI Upper p-value
ACME (control) 2.350 1.881 2.85 <2e-16 ***
ACME (treated) 2.350 1.881 2.85 <2e-16 ***
ADE (control) 1.223 0.787 1.66 <2e-16 ***
ADE (treated) 1.223 0.787 1.66 <2e-16 ***
Total Effect 3.573 3.001 4.13 <2e-16 ***
Prop. Mediated (control) 0.657 0.557 0.76 <2e-16 ***
Prop. Mediated (treated) 0.657 0.557 0.76 <2e-16 ***
ACME (average) 2.350 1.881 2.85 <2e-16 ***
ADE (average) 1.223 0.787 1.66 <2e-16 ***
Prop. Mediated (average) 0.657 0.557 0.76 <2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Sample Size Used: 770
Simulations: 10000
#mediation analysis for control and remitted
med_neg_rem_rem <- mediation::mediate(med_fit_h2_neg_rem, out_fit_h2_neg_rem, treat = 'condition_dummy' , control.value = 1, treat.value = 2 , mediator = 'rem_mem_neg_mood_lag', sims=10000)
summary(med_neg_rem_rem)
Causal Mediation Analysis
Quasi-Bayesian Confidence Intervals
Mediator Groups: subjectcode
Outcome Groups: subjectcode
Output Based on Overall Averages Across Groups
Estimate 95% CI Lower 95% CI Upper p-value
ACME (control) 0.894 0.546 1.27 <2e-16 ***
ACME (treated) 0.894 0.546 1.27 <2e-16 ***
ADE (control) 0.424 0.142 0.70 0.0032 **
ADE (treated) 0.424 0.142 0.70 0.0032 **
Total Effect 1.318 0.886 1.76 <2e-16 ***
Prop. Mediated (control) 0.678 0.505 0.87 <2e-16 ***
Prop. Mediated (treated) 0.678 0.505 0.87 <2e-16 ***
ACME (average) 0.894 0.546 1.27 <2e-16 ***
ADE (average) 0.424 0.142 0.70 0.0032 **
Prop. Mediated (average) 0.678 0.505 0.87 <2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Sample Size Used: 770
Simulations: 10000
#create new dataframes with only control remitted and only control depressed
df_medal_con_rem = df_medal_clean %>% filter(condition=='control' | condition == 'remitted')
df_medal_con_dep = df_medal_clean %>% filter(condition=='control' | condition == 'depressed')
#remitted group
df_medal_con_rem$condition_dummy = build_contrast(df_medal_con_rem$condition, 'control', 'remitted')
med_h2_neg_rem_rem <- mdt_moderated(data = df_medal_con_rem, IV = condition_dummy, DV = neg_mood, M = rem_mem_neg_mood_lag, Mod = neg_mood_lag_rem)
rbindlist(med_h2_neg_rem_rem$paths, idcol = T)
#depressed group
df_medal_con_dep$condition_dummy = build_contrast(df_medal_con_dep$condition, 'control', 'depressed')
med_h2_neg_rem_dep <- mdt_moderated(data = df_medal_con_dep, IV = condition_dummy, DV = neg_mood, M = rem_mem_neg_mood_lag, Mod = neg_mood_lag_rem)
rbindlist(med_h2_neg_rem_dep$paths,idcol = T)
NA
Context
asis_output(tab_model( (lmer(pos_mood_cs ~ rec_mem_pos_mood_cs_lag*context_location + (1 | subjectcode), data = df_medal_clean)),
(lmer(pos_mood_cs ~ rem_mem_pos_mood_cs_lag*context_location + (1 | subjectcode), data = df_medal_clean)),
(lmer(neg_mood_cs ~ rem_mem_neg_mood_cs_lag*context_location + (1 | subjectcode), data = df_medal_clean)),
(lmer(neg_mood_cs ~ rem_mem_neg_mood_cs_lag*context_location + (1 | subjectcode), data = df_medal_clean)))$knitr)
boundary (singular) fit: see help('isSingular')
boundary (singular) fit: see help('isSingular')
boundary (singular) fit: see help('isSingular')
boundary (singular) fit: see help('isSingular')
|
|
pos mood cs
|
pos mood cs
|
neg mood cs
|
neg mood cs
|
|
Predictors
|
Estimates
|
CI
|
p
|
Estimates
|
CI
|
p
|
Estimates
|
CI
|
p
|
Estimates
|
CI
|
p
|
|
(Intercept)
|
3.80
|
3.44 – 4.15
|
<0.001
|
5.35
|
4.64 – 6.05
|
<0.001
|
4.77
|
4.11 – 5.44
|
<0.001
|
4.77
|
4.11 – 5.44
|
<0.001
|
|
rec mem pos mood cs lag
|
0.49
|
0.44 – 0.54
|
<0.001
|
|
|
|
|
|
|
|
|
|
|
context location
|
0.01
|
-0.13 – 0.14
|
0.906
|
-0.25
|
-0.53 – 0.02
|
0.068
|
-0.19
|
-0.44 – 0.06
|
0.136
|
-0.19
|
-0.44 – 0.06
|
0.136
|
rec mem pos mood cs lag *
context location
|
0.01
|
-0.01 – 0.03
|
0.538
|
|
|
|
|
|
|
|
|
|
|
rem mem pos mood cs lag
|
|
|
|
0.33
|
0.21 – 0.45
|
<0.001
|
|
|
|
|
|
|
rem mem pos mood cs lag *
context location
|
|
|
|
0.05
|
0.01 – 0.10
|
0.023
|
|
|
|
|
|
|
|
rem mem neg mood cs lag
|
|
|
|
|
|
|
0.26
|
0.14 – 0.39
|
<0.001
|
0.26
|
0.14 – 0.39
|
<0.001
|
rem mem neg mood cs lag *
context location
|
|
|
|
|
|
|
0.04
|
-0.01 – 0.08
|
0.129
|
0.04
|
-0.01 – 0.08
|
0.129
|
|
Random Effects
|
|
σ2
|
1.28
|
1.48
|
1.45
|
1.45
|
|
τ00
|
0.00 subjectcode
|
0.00 subjectcode
|
0.00 subjectcode
|
0.00 subjectcode
|
|
N
|
191 subjectcode
|
188 subjectcode
|
188 subjectcode
|
188 subjectcode
|
|
Observations
|
3146
|
789
|
789
|
789
|
|
Marginal R2 / Conditional R2
|
0.257 / NA
|
0.163 / NA
|
0.100 / NA
|
0.100 / NA
|
SRET
In addition to the EMA, participants also completed the SRET. This is
a lab based memory bias measure. We want to check whether our memory
bias measures from real-life are linked to ones from the lab in this
supplementary analysis.
library('haven')
Warning: package ‘haven’ was built under R version 4.2.3
sret_file = file.path("Z:/", "inbox", "transfer-2023-12-07-02-15-pm", 'MEDAL_pre and post quest and remote recall_workfile!!_for paper only var.sav')
sret_data = read_sav(sret_file)
sret_data = sret_data %>% select(c(1,4)) %>% distinct()
df_medal_rec_cor1 = df_medal_clean %>%
dplyr::select(subjectcode, pos_mood, rec_mem_pos_mood_lag) %>%
na.omit() %>%
dplyr::group_by(subjectcode) %>%
dplyr::mutate(corr = tryCatch(cor(rec_mem_pos_mood_lag, pos_mood), warning = function(e) NA),
model_type = "PA_REC") %>%
dplyr::select(subjectcode, corr, model_type) %>%
dplyr::distinct() %>% ungroup()
df_medal_rec_cor2 = df_medal_clean %>%
dplyr::select(subjectcode, neg_mood, rec_mem_neg_mood_lag) %>%
na.omit() %>%
dplyr::group_by(subjectcode) %>%
dplyr::mutate(corr = tryCatch(cor(rec_mem_neg_mood_lag, neg_mood), warning = function(e) NA),
model_type = "NA_REC") %>%
dplyr::select(subjectcode, corr, model_type) %>%
dplyr::distinct() %>% ungroup()
df_medal_rem_cor1 = df_medal_clean %>%
dplyr::select(subjectcode, pos_mood, rem_mem_pos_mood_lag) %>%
na.omit() %>%
dplyr::group_by(subjectcode) %>%
dplyr::mutate(corr = tryCatch(cor(rem_mem_pos_mood_lag, pos_mood), warning = function(e) NA),
model_type = "PA_REM") %>%
dplyr::select(subjectcode, corr, model_type) %>%
dplyr::distinct()
df_medal_rem_cor2 = df_medal_clean %>%
dplyr::select(subjectcode, neg_mood, rem_mem_neg_mood_lag) %>%
na.omit() %>%
dplyr::group_by(subjectcode) %>%
dplyr::mutate(corr = tryCatch(cor(rem_mem_neg_mood_lag, neg_mood), warning = function(e) NA),
model_type = "NA_REM") %>%
dplyr::select(subjectcode, corr, model_type) %>%
dplyr::distinct()
df_cors = rbindlist(list(df_medal_rec_cor1, df_medal_rec_cor2, df_medal_rem_cor1, df_medal_rem_cor2))
df_ref = merge(sret_data, df_cors, by.x = 'subject', by.y = 'subjectcode')
df_ref = df_ref %>% rename(SRET = SRET_BiasGotlib_neg) %>% dplyr::mutate(group = ifelse(subject<800, "remitted",
ifelse(subject>=900, "control", 'depressed')) )
for (mod_type in c("PA_REC", "PA_REM", "NA_REC", "NA_REM")){
print(mod_type)
print(summary(lm(SRET ~ corr*group, data = df_ref %>% filter(model_type == mod_type))))
}
[1] "PA_REC"
Call:
lm(formula = SRET ~ corr * group, data = df_ref %>% filter(model_type ==
mod_type))
Residuals:
Min 1Q Median 3Q Max
-0.42802 -0.18783 -0.03731 0.07877 0.96612
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.032066 0.055434 0.578 0.5637
corr 0.009979 0.109528 0.091 0.9275
groupdepressed 0.207749 0.113678 1.828 0.0694 .
groupremitted 0.109699 0.078769 1.393 0.1655
corr:groupdepressed 0.299086 0.203331 1.471 0.1431
corr:groupremitted 0.145614 0.151633 0.960 0.3383
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.2605 on 171 degrees of freedom
(14 observations deleted due to missingness)
Multiple R-squared: 0.2304, Adjusted R-squared: 0.2079
F-statistic: 10.24 on 5 and 171 DF, p-value: 1.343e-08
[1] "PA_REM"
Call:
lm(formula = SRET ~ corr * group, data = df_ref %>% filter(model_type ==
mod_type))
Residuals:
Min 1Q Median 3Q Max
-0.41550 -0.21434 -0.03978 0.12018 0.96298
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.034360 0.044794 0.767 0.444147
corr 0.008142 0.065661 0.124 0.901462
groupdepressed 0.365632 0.065231 5.605 8.57e-08 ***
groupremitted 0.192437 0.056284 3.419 0.000792 ***
corr:groupdepressed 0.007906 0.096231 0.082 0.934621
corr:groupremitted -0.025126 0.082845 -0.303 0.762047
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.2675 on 165 degrees of freedom
(17 observations deleted due to missingness)
Multiple R-squared: 0.2043, Adjusted R-squared: 0.1802
F-statistic: 8.475 on 5 and 165 DF, p-value: 3.685e-07
[1] "NA_REC"
Call:
lm(formula = SRET ~ corr * group, data = df_ref %>% filter(model_type ==
mod_type))
Residuals:
Min 1Q Median 3Q Max
-0.45379 -0.17704 -0.03939 0.08131 0.96345
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.03215 0.04477 0.718 0.473691
corr 0.01727 0.10065 0.172 0.863976
groupdepressed 0.29548 0.08311 3.555 0.000489 ***
groupremitted 0.10471 0.06401 1.636 0.103709
corr:groupdepressed 0.14637 0.15930 0.919 0.359496
corr:groupremitted 0.18205 0.13735 1.325 0.186793
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.2605 on 170 degrees of freedom
(15 observations deleted due to missingness)
Multiple R-squared: 0.2323, Adjusted R-squared: 0.2097
F-statistic: 10.29 on 5 and 170 DF, p-value: 1.249e-08
[1] "NA_REM"
Call:
lm(formula = SRET ~ corr * group, data = df_ref %>% filter(model_type ==
mod_type))
Residuals:
Min 1Q Median 3Q Max
-0.43367 -0.17164 -0.04881 0.12752 0.94467
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.04423 0.04128 1.071 0.285714
corr -0.02001 0.06467 -0.309 0.757395
groupdepressed 0.34096 0.06427 5.305 3.86e-07 ***
groupremitted 0.19828 0.05408 3.667 0.000338 ***
corr:groupdepressed 0.08919 0.10774 0.828 0.409041
corr:groupremitted -0.07917 0.08544 -0.927 0.355620
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.2702 on 154 degrees of freedom
(28 observations deleted due to missingness)
Multiple R-squared: 0.2103, Adjusted R-squared: 0.1846
F-statistic: 8.201 on 5 and 154 DF, p-value: 6.88e-07
Relative Bias
Here we want to check for the relative NA/PA bias. So we extact the
random effects from the models to get the slopes for each subject, and
then compare the NA/PA slopes. This tells us if theres a bias between
negative and positive memory.
# Get individual slopes
pos_rec_ref = as.data.frame(ranef(h1_pos_recent_models[[2]])) %>% filter(term!='(Intercept)') %>% select(c(3:5))
pos_rem_ref = as.data.frame(ranef(h1_pos_remote_models[[2]])) %>% filter(term!='(Intercept)') %>% select(c(3:5))
neg_rec_ref = as.data.frame(ranef(h1_neg_recent_models[[4]])) %>% filter(term!='(Intercept)') %>% select(c(3:5))
neg_rem_ref = as.data.frame(ranef(h1_neg_remote_models[[4]])) %>% filter(term!='(Intercept)') %>% select(c(3:5))
pos_rec_ref$mod = "PA_REC"
pos_rem_ref$mod = "PA_REM"
neg_rec_ref$mod = "NA_REC"
neg_rem_ref$mod = "NA_REM"
# Put together into a new data frame
df_ref = rbindlist(list(pos_rec_ref, pos_rem_ref, neg_rec_ref, neg_rem_ref))
df_ref = df_ref %>%
dplyr::mutate(sub = as.numeric(as.character(grp))) %>%
dplyr::mutate(group = ifelse(sub <= 800, "remitted",
ifelse(sub>=900, "control", 'depressed')) )
df_ref_rec = df_ref %>% filter(mod == 'PA_REC' | mod=='NA_REC')
df_ref_rem = df_ref %>% filter(mod == 'PA_REM' | mod=='NA_REM')
summary(lmer(condsd ~ mod*group + (1|sub), data = df_ref_rec))
Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: condsd ~ mod * group + (1 | sub)
Data: df_ref_rec
REML criterion at convergence: -1628.1
Scaled residuals:
Min 1Q Median 3Q Max
-5.2099 -0.1327 0.0034 0.2821 2.0044
Random effects:
Groups Name Variance Std.Dev.
sub (Intercept) 7.503e-05 0.008662
Residual 5.543e-04 0.023543
Number of obs: 370, groups: sub, 185
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 0.174483 0.003446 358.898081 50.636 < 2e-16 ***
modPA_REC -0.151757 0.004573 182.000000 -33.182 < 2e-16 ***
groupdepressed -0.031232 0.005055 358.898081 -6.178 1.76e-09 ***
groupremitted -0.019837 0.004381 358.898081 -4.528 8.11e-06 ***
modPA_REC:groupdepressed 0.028693 0.006709 182.000000 4.277 3.06e-05 ***
modPA_REC:groupremitted 0.018448 0.005814 182.000000 3.173 0.00177 **
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr) mdPA_REC grpdpr grprmt mdPA_REC:grpd
modPA_REC -0.664
groupdprssd -0.682 0.452
groupremttd -0.787 0.522 0.536
mdPA_REC:grpd 0.452 -0.682 -0.664 -0.356
mdPA_REC:grpr 0.522 -0.787 -0.356 -0.664 0.536
summary(lmer(condsd ~ mod*group + (1|sub), data = df_ref_rem))
Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: condsd ~ mod * group + (1 | sub)
Data: df_ref_rem
REML criterion at convergence: -3053.2
Scaled residuals:
Min 1Q Median 3Q Max
-4.6559 -0.3184 0.0410 0.5489 1.7622
Random effects:
Groups Name Variance Std.Dev.
sub (Intercept) 2.596e-06 0.001611
Residual 9.576e-06 0.003094
Number of obs: 368, groups: sub, 184
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 4.997e-02 4.792e-04 3.463e+02 104.265 < 2e-16 ***
modPA_REM -1.178e-02 6.011e-04 1.810e+02 -19.601 < 2e-16 ***
groupdepressed 6.628e-05 7.030e-04 3.463e+02 0.094 0.9249
groupremitted -1.069e-04 6.106e-04 3.463e+02 -0.175 0.8612
modPA_REM:groupdepressed -3.553e-03 8.818e-04 1.810e+02 -4.029 8.22e-05 ***
modPA_REM:groupremitted -1.525e-03 7.659e-04 1.810e+02 -1.991 0.0479 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr) mdPA_REM grpdpr grprmt mdPA_REM:grpd
modPA_REM -0.627
groupdprssd -0.682 0.428
groupremttd -0.785 0.492 0.535
mdPA_REM:grpd 0.428 -0.682 -0.627 -0.336
mdPA_REM:grpr 0.492 -0.785 -0.336 -0.627 0.535
Plots
First we set our theme and fix the data for the figures we want
# Theme
ggtheme = theme(text = element_text(size = 8),
panel.background = element_rect(fill = "transparent"),
panel.grid.major = element_line(color = "grey90"),
panel.grid.minor = element_line(color = "grey100"),
panel.border = element_rect(color = "grey80", fill = "transparent"))
# Estimate the SD
df_medal_clean2 = df_medal_clean %>%
mutate(pos_mood_sd = ifelse( (pos_mood_cs_lag_rec > (mean(pos_mood_cs_lag_rec, na.rm=T)+sd(pos_mood_cs_lag_rec, na.rm=T))), "+1SD",
ifelse( (pos_mood_cs_lag_rec < (mean(pos_mood_cs_lag_rec, na.rm=T)-sd(pos_mood_cs_lag_rec, na.rm =T))), "-1SD", "Mean"))) %>%
mutate(neg_mood_sd = ifelse( (neg_mood_cs_lag_rec > (mean(neg_mood_cs_lag_rec, na.rm=T)+sd(neg_mood_cs_lag_rec, na.rm=T))), "+1SD",
ifelse( (neg_mood_cs_lag_rec < (mean(neg_mood_cs_lag_rec, na.rm=T)-sd(neg_mood_cs_lag_rec, na.rm =T))), "-1SD", "Mean")))
# Fix factor levels
df_medal_clean2$Condition = factor(df_medal_clean2$condition, levels=c('control', 'remitted', 'depressed' ), labels=c('Never-Depressed', 'Remitted', 'Depressed'))
df_medal_clean2$pos_mood_sd = factor(df_medal_clean2$pos_mood_sd, levels=c('-1SD', 'Mean', '+1SD' ))
df_medal_clean2$neg_mood_sd = factor(df_medal_clean2$neg_mood_sd, levels=c('-1SD', 'Mean', '+1SD' ))
PA-CUR-REC
# Plot
plot_pa_rec = ggplot(df_medal_clean2, aes(x = rec_mem_pos_mood_c_lag, y=pos_mood_c, color = pos_mood_sd, fill = pos_mood_sd)) +
geom_smooth(method = 'lm', alpha = 0.15) +
labs( x = bquote(paste(REC[PA], " (subject-centered a.u.)")), y = "PA (subject-centered a.u.)", color = bquote(CUR[PA]), fill = bquote(CUR[PA]) ) +
ggtheme +
scale_color_manual(values = wesanderson::wes_palette(n=3, name='GrandBudapest2' )) +
scale_fill_manual(values = wesanderson::wes_palette(n=3, name='GrandBudapest2' )) +
facet_grid(.~Condition)
plot_pa_rec
PA-CUR-REC
# Plot
plot_na_rec =
ggplot(df_medal_clean2, aes(x = rec_mem_neg_mood_c_lag, y=neg_mood_c, color = neg_mood_sd, fill = neg_mood_sd)) +
geom_smooth(method = 'lm', alpha = 0.15) +
labs( x = bquote(paste(REC['NA'], " (subject-centered a.u.)")), y = "NA (subject-centered a.u.)", color = bquote(CUR["NA"]), fill = bquote(CUR["NA"]) ) +
scale_color_manual(values = wesanderson::wes_palette(n=3, name='GrandBudapest2' )) +
scale_fill_manual(values = wesanderson::wes_palette(n=3, name='GrandBudapest2' )) +
ggtheme +
facet_grid(.~Condition)
plot_na_rec
Combined Plot for
pub
ggarrange(NA, plot_pa_rec, NA, plot_na_rec,
widths=c(0.05, 1, 0.05, 1), ncol = 2, nrow = 2,
labels=c("A", NA, "B"))
Warning: Cannot convert object of class logical into a grob.`geom_smooth()` using formula = 'y ~ x'Warning: Removed 4247 rows containing non-finite values (`stat_smooth()`).Warning: Cannot convert object of class logical into a grob.`geom_smooth()` using formula = 'y ~ x'Warning: Removed 4249 rows containing non-finite values (`stat_smooth()`).
ggsave("figures/fig1_threeway.pdf", device = 'pdf', dpi = 320, height = 6)
Saving 7.3 x 6 in image
---
title: 'Emotional Memory in Daily Life'
author:
- Noa Magusin
- Rayyan Tutunji
date: "`r Sys.Date()`"
output:
  html_notebook:
    fig_width: 8
    fig_height: 8
    toc: yes
    toc_float: yes
    number_sections: yes
    theme: flatly
  html_document:
    toc: yes
    df_print: paged
---

# Introduction

This notebook contains analyses from the MEDAL study, assessing memory bias in remitted and depressed individuals. The pre-registration for the main analyses, as well as a preprint version of the accompanying article can be found [here](https://osf.io/zufjs/) as well. The main aim of the current work was to examine how three groups (control, remitted, depressed) differ in terms of emotional memory dynamics in a real-life setting.

```{r message=TRUE, warning=FALSE}
#Load packages 
#renv::activate()
library(dplyr)
library(janitor)
library(readxl)
library(data.table)
library(ggplot2)
library(psych)
library(devtools)
library(pastecs)
library(cli)
library(lmerTest) #linear mixed models 
library(performance) #fits of residuals 
library(DescTools)
library(tidyr) # Separate trigger column
library(sjPlot) # for nice tables and interaction plots 
library(knitr) # to print the tables within notebook
library(foreach) #run parallel loops
library(parallel)
library(doParallel) #run parallel loops
library(ggpubr) # emmeans
library(mediation) #mediation analysis 
library(plotly) #for interactive plots
library(jtools) #for theme_apa
library(JSmediation) #moderated mediation
library(wesanderson) 
source("functions.R")
```

# Data

Here we load in our data and preprocess it in steps. We first need to separate the different surveys that were delivered to bring them into the same sampling times, merge in some descriptives we need as covariates, and estimate subject-centered and lagged variables for our models later. 


1. Load and clean data

```{r}
# Load the data
MEDAL_ESM_compleet <- read_excel("data/MEDAL_ESM_compleet.xlsx")
# Move variables around and clean names
MEDAL_ESM_compleet= MEDAL_ESM_compleet %>%
  rename(id='Movisens-ID') %>% 
  clean_names() %>%
  rename(subjectcode = 'deelnemersnr') %>% 
  relocate('subjectcode', .after = 'id')
df_medal_try = MEDAL_ESM_compleet

```

2. Split different EMA surveys we have

```{r}
# Sleep EMA Qs
df_medal_sleep = df_medal_try %>% 
  filter(form=='Slaap') %>% 
  dplyr::select(c(1, 2, 4, 5, 7, 9, 14)) 
# Recent EMA Qs
df_medal_recent = df_medal_try %>% 
  filter(form=='Recent') %>% 
  dplyr::select(c(1, 2, 4, 5, 7, 9, contains('rec')))
# Remote EMA Qs
df_medal_remote = df_medal_try %>%
  filter(form=='Remote') %>%
  dplyr::select(c(1, 2, 4, 5, 7, 9, contains('rem')))
# Mastery EMA Qs
df_medal_mastery = df_medal_try %>%
  filter(form=='Mastery') %>% 
  dplyr::select(c(1, 2, 4, 5, 7, 9, contains('master')))
# Standard Qs
df_medal_standard = df_medal_try %>%
  filter(form=='Standaard')  %>%
  dplyr::select(c(1, 2, 4, 5, 7, 9, (-contains('mastery') & -contains('rem') & -contains('rec')))) %>% 
  dplyr::select(-c(7,13,14))

# merge sleep and standard
df_medal_merged = merge(df_medal_sleep, df_medal_standard, by=c('id', 'trigger_counter', 'subjectcode'), suffixes = c("_sleep", ""), all=T )

# merge to recent, remote and mastery
df_medal_merged= merge(df_medal_merged, df_medal_recent, by=c('id', 'trigger_counter', 'subjectcode' ), suffixes = c('', '_recent'), all = T )
df_medal_merged = merge(df_medal_merged, df_medal_remote, by=c('id', 'trigger_counter', 'subjectcode' ), suffixes = c('', '_remote'), all = T )
df_medal_merged = merge(df_medal_merged, df_medal_mastery, by=c('id', 'trigger_counter', 'subjectcode' ), suffixes = c('', '_mastery'), all = T )

# Fix start dates and times
df_medal_merged$form_start_date <- as.character(df_medal_merged$form_start_date)
df_medal_merged$form_start_date <- as.ITime(df_medal_merged$form_start_date)
df_medal_merged = df_medal_merged %>% 
  dplyr::mutate(form_start_date = ifelse(is.na(form_start_date), form_start_date_sleep, form_start_date))
```

3. Add variables on Education, Gender, and Age

```{r}
# Load in previous data for demographics (also contains EMA)
load("data/IPAQ_Maeve_Workspace.RData")
df_MEDAL <- df_MEDAL[-c(192), ]

# Select relevant demo info
df_MEDAL = df_MEDAL %>% 
  dplyr::select(1, 3, 5:7) %>% 
  rename('subjectcode' = 'subject', 'education' = 'Education3groups', 'n_episodes'='NumberEpisodes' )

# merge demo to full EMA
df_medal_merged =  merge(df_medal_merged, df_MEDAL, by = c('subjectcode'), all = T ) %>%
  clean_names() 

df_medal_merged$education = as.factor(df_medal_merged$education)
df_medal_merged$gender = as.factor(df_medal_merged$gender)

df_medal_merged = df_medal_merged %>% 
  relocate(c('id', 'gender', 'age', 'education'), .after = 'subjectcode') %>% 
  relocate ('age', .after = 'gender') %>% 
  relocate ('education', .after = 'age') %>% 
  relocate ('id', .before = 'subjectcode')

```

4. Categorize the subjectcode into the three conditions (Depressed, Remitted, Control (Never-depressed))

```{r}
df_medal_merged$condition = case_when(df_medal_merged$subjectcode >= 700 & df_medal_merged$subjectcode < 800 ~ "remitted",
                                      df_medal_merged$subjectcode >= 800 & df_medal_merged$subjectcode < 900 ~ "depressed", 
                                      df_medal_merged$subjectcode >= 900 ~ "control")
df_medal_merged = df_medal_merged %>% relocate ('condition', .after = 'subjectcode' )
df_medal_merged$condition <- as.factor(df_medal_merged$condition)
```

5. Calculate reaction time 

```{r}
df_medal_merged$rt_sleep = lubridate::as.difftime( df_medal_merged$form_finish_date_sleep - df_medal_merged$form_start_date_sleep)
```

6. Separate time from when trigger was sent

```{r}

df_medal_merged = df_medal_merged %>% 
  tidyr::separate(trigger, c('Random', 'Time', 'Trigger_Time_1', 'Trigger_Time_2'))  %>% 
  dplyr::mutate(trigger_time = paste(Trigger_Time_1, Trigger_Time_2, sep=':')) %>%#separate time from previous trigger
  dplyr::mutate(trigger = paste(Random, Time, trigger_time, sep=' ')) #bring back 'trigger' column 

#Turn numbers into time format
df_medal_merged$trigger_time <- as.ITime(df_medal_merged$trigger_time)
```

7. Find the trigger number per day

```{r}
#df_medal_merged$trigger_counter[is.na(df_medal_merged$trigger_time)] <- 1

df_medal_merged$trigger_number[is.na(df_medal_merged$trigger_time)] <- 1 
df_medal_merged$trigger_number[df_medal_merged$trigger_time >= (as.ITime("10:00:00"))& df_medal_merged$trigger_time < (as.ITime("12:00:00"))] <- 2
df_medal_merged$trigger_number[df_medal_merged$trigger_time >= (as.ITime("12:00:00"))& df_medal_merged$trigger_time < (as.ITime("14:00:00"))] <- 3
df_medal_merged$trigger_number[df_medal_merged$trigger_time >= (as.ITime("14:00:00"))& df_medal_merged$trigger_time < (as.ITime("16:00:00"))] <- 4
df_medal_merged$trigger_number[df_medal_merged$trigger_time >= (as.ITime("16:00:00"))& df_medal_merged$trigger_time < (as.ITime("18:00:00"))] <- 5
df_medal_merged$trigger_number[df_medal_merged$trigger_time >= (as.ITime("18:00:00"))& df_medal_merged$trigger_time < (as.ITime("20:00:00"))] <- 6
df_medal_merged$trigger_number[df_medal_merged$trigger_time >= (as.ITime("20:00:00"))& df_medal_merged$trigger_time < (as.ITime("23:00:01"))] <- 7

# df
df_medal_merged = df_medal_merged %>% 
  relocate ('trigger_number', .after = 'trigger_counter')%>% relocate('trigger_time', .before ='form_start_date') %>%
  relocate('trigger', .before ='trigger_time')
```

8. Final cleaning of the dataset
```{r}

#Create dataset where double morning list is removed 
df_medal_merged = df_medal_merged %>% 
  dplyr::mutate(trigger_counter=ifelse(trigger_number==1 & lag(trigger_number)==1, lag(trigger_counter), trigger_counter)) %>% 
  dplyr::mutate(trigger_counter=ifelse(trigger_number==1 & lag(trigger_number)==1, lag(trigger_counter), trigger_counter)) %>%
  dplyr::mutate(trigger_counter=ifelse(trigger_number==1 & lag(trigger_number)==1, lag(trigger_counter), trigger_counter)) %>%
  dplyr::mutate(trigger_counter=ifelse(trigger_number==1 & lag(trigger_number)==1, lag(trigger_counter), trigger_counter)) %>% 
  dplyr::mutate(trigger_counter=ifelse(trigger_number==1 & lag(trigger_number)==1, lag(trigger_counter), trigger_counter))

# now we have the clean dataframe, remove duplicated coloumns
df_medal_clean = df_medal_merged %>%
  dplyr::group_by(subjectcode) %>% 
  distinct(trigger_counter, .keep_all = TRUE) %>% 
  dplyr::select (1:12, 17:60) %>% 
  dplyr::mutate(original_order = row_number()) %>% 
  relocate('original_order', .before = 'id')

#Set the order of the factor to control, remission, depression (for visual purposes)
df_medal_clean$condition <- factor(df_medal_clean$condition, levels=c('control', 'remitted', 'depressed' ), labels=c('control', 'remitted', 'depressed'))

# Create dummy variable 
df_medal_clean = df_medal_clean %>% 
  mutate(condition_dummy = case_when(condition == 'depressed'~ 3, condition == 'remitted'~ 2, condition == 'control' ~ 1))

df_medal_clean$condition_dummy = as.factor(df_medal_clean$condition_dummy)

#set weekday 
df_medal_clean$form_finish_date <- as.character(df_medal_clean$form_finish_date)
df_medal_clean$form_finish_date <- strptime(df_medal_clean$form_finish_date, format = '%Y-%m-%d %H:%M:%S')

df_medal_clean$finish_date <- as.Date(df_medal_clean$form_finish_date)
df_medal_clean$weekday <- as.integer(format(df_medal_clean$finish_date, '%w'))
df_medal_clean = df_medal_clean %>% relocate('weekday', .after = 'trigger_number')

#solve duplicate issue 
df_medal_duplicate = df_medal_clean %>% dplyr::group_by(subjectcode, weekday) %>% filter(duplicated(trigger_number))

  #since row 10 for subject 712 is a duplicate morning list
  df_medal_clean <- subset(df_medal_clean, !(subjectcode == 712 & original_order == 10))
  df_medal_clean = df_medal_clean %>% dplyr::group_by(subjectcode, weekday) %>% 
    mutate(temp = lead(trigger_number), 
           trigger_number = case_when(trigger_number == temp ~ (temp -1), TRUE ~ trigger_number)) %>%
    select(-temp)
  #Take the rows out that have 'NA'for the weekday since form wasn't finished and there is no data for PA, NA, or Memory
  df_medal_clean = df_medal_clean %>% filter(!is.na(weekday))
  df_medal_clean$original_order <- 1:nrow(df_medal_clean) 

# Create expanded df with all potential datapoints

df_medal_day = df_medal_clean %>% 
  dplyr::select('subjectcode', 'weekday', 'original_order') %>% 
  dplyr::mutate(weekday = as.numeric(weekday)) %>%
  dplyr::group_by(subjectcode) %>%
  dplyr:: distinct(weekday, .keep_all =T) %>% 
  dplyr::ungroup()

df_medal_day = df_medal_day %>% 
  slice(rep(1:n(), each = 7)) %>% 
  dplyr::mutate(trigger_number = rep(1:7, length.out=n())) %>% 
  dplyr::rename(order = original_order)


#merge with main dataframe
df_medal_clean =  merge(df_medal_day, df_medal_clean, by=c('subjectcode', 'trigger_number', 'weekday'), all= T) 
df_medal_clean = df_medal_clean %>% mutate(order = as.numeric(order))
df_medal_clean = arrange(df_medal_clean, order)
```

9. Centre, scale, and average Variables 

```{r warning=FALSE}

used_vars = c("neg_mood", "rec_mem_neg_mood", "rem_mem_neg_mood", "pos_mood", "rec_mem_pos_mood", "rem_mem_pos_mood")

#Centering & scaling 
df_medal_clean = df_medal_clean %>% 
  dplyr::group_by(subjectcode) %>%
  # Center and scale
  dplyr::mutate( across(used_vars, ~ (.x/10), .names = "{.col}" ),
                 across(used_vars, ~ mean(.x, na.rm=T), .names = "{.col}_m" ),
                 across(used_vars, ~ (.x - mean(.x, na.rm=T)), .names = "{.col}_c" ), 
                 across(paste0(used_vars, "_c"), ~ DescTools::Winsorize(.x, na.rm = T), .names = "{.col}")) %>%  
  ungroup() %>%
  # Rescale to positive
  mutate(across(c(paste0(used_vars,"_c")), ~ abs(min(.x, na.rm = T)) + 1 + .x, .names = "{.col}s"))

```


10. Lag variables 
```{r}
# Remote 

df_medal_clean = df_medal_clean %>%
  dplyr::group_by(subjectcode) %>%
  mutate(rem_mem_pos_mood_lag = lead(rem_mem_pos_mood, n=7, order_by=subjectcode),
         rem_mem_neg_mood_lag = lead(rem_mem_neg_mood, n=7, order_by=subjectcode),
         pos_mood_lag_rem = lead(pos_mood, n=7, order_by=subjectcode),
         neg_mood_lag_rem = lead(neg_mood, n=7, order_by=subjectcode), 
         rem_mem_pos_mood_cs_lag = lead(rem_mem_pos_mood_cs, n=7, order_by=subjectcode),
         rem_mem_neg_mood_cs_lag = lead(rem_mem_neg_mood_cs, n=7, order_by=subjectcode),
         pos_mood_cs_lag_rem = lead(pos_mood_cs, n=7, order_by=subjectcode),
         neg_mood_cs_lag_rem = lead(neg_mood_cs, n=7, order_by=subjectcode),
         rem_mem_pos_mood_c_lag = lead(rem_mem_pos_mood_c, n=7, order_by=subjectcode),
         rem_mem_neg_mood_c_lag = lead(rem_mem_neg_mood_c, n=7, order_by=subjectcode),
         pos_mood_c_lag_rem = lead(pos_mood_c, n=7, order_by=subjectcode),
         neg_mood_c_lag_rem = lead(neg_mood_c, n=7, order_by=subjectcode)) %>% 
  filter(!is.na(original_order)) # filter out the NA rows for the lagging of recent memory 

# Recent
df_medal_clean = df_medal_clean %>% 
  dplyr::group_by(subjectcode) %>%
  mutate(rec_mem_pos_mood_lag = lead(rec_mem_pos_mood, n=1, order_by=subjectcode),
         rec_mem_neg_mood_lag = lead(rec_mem_neg_mood, n=1, order_by=subjectcode),
         pos_mood_lag_rec = lead(pos_mood, n=1, order_by=subjectcode),
         neg_mood_lag_rec = lead(neg_mood, n=1, order_by=subjectcode),
         rec_mem_pos_mood_cs_lag = lead(rec_mem_pos_mood_cs, n=1, order_by=subjectcode),
         rec_mem_neg_mood_cs_lag = lead(rec_mem_neg_mood_cs, n=1, order_by=subjectcode),
         pos_mood_cs_lag_rec = lead(pos_mood_cs, n=1, order_by=subjectcode),
         neg_mood_cs_lag_rec = lead(neg_mood_cs, n=1, order_by=subjectcode),
         rec_mem_pos_mood_c_lag = lead(rec_mem_pos_mood_c, n=1, order_by=subjectcode),
         rec_mem_neg_mood_c_lag = lead(rec_mem_neg_mood_c, n=1, order_by=subjectcode),
         pos_mood_c_lag_rec = lead(pos_mood_c, n=1, order_by=subjectcode),
         neg_mood_c_lag_rec = lead(neg_mood_c, n=1, order_by=subjectcode)) %>% 
  ungroup()
  

```

# Descriptives {.tabset}

We first plot some general population descriptives, followed by some descriptives of the scales we use in the EMA weeks and compliance rates.

## Population Descriptives {-}
```{r fig.height=12, fig.width=20}
#Create Descriptives Tables
summary_table = df_medal_clean %>% distinct(subjectcode, .keep_all = TRUE) %>% group_by(condition) %>% summarise(
  'N' = n(),
  'Age \n M +/- SD' = paste0(round(mean(age, na.rm =T), 1), ' +/- ' , round(sd(age, na.rm =T), 1)),
  #'NA Age' = sum(is.na(age)), 
  '% Education Low' = mean(education == 0, na.rm = T)*100,
  '% Education Middle' = mean(education == 1, na.rm = T)*100,
  '% Education High' = mean(education == 2, na.rm = T)*100,
  #'NA Education' = sum(is.na(education)), 
  '% Female' = mean(gender ==1, na.rm = T)*100,
  #'NA Gender' = sum(is.na(gender))
)

#Compliance rates
df_medal_compliance_individual = df_medal_clean %>% group_by(condition, subjectcode) %>% summarise('ComplianceRate' = sum(!is.na(trigger_number))/42*100)

length(which(df_medal_compliance_individual$ComplianceRate <70))

df_medal_compliance = df_medal_clean %>% group_by(condition) %>% summarise('% Mean Compliance' = sum(!is.na(trigger_number))/(n_distinct(subjectcode) *42)*100) 


# Create a table
summary_table = summary_table %>% left_join(df_medal_compliance, by = 'condition')
rempsyc::nice_table(summary_table)


#create dataset with only unique subjectcode 
df_medal_distinct = df_medal_clean %>% distinct(subjectcode, .keep_all = TRUE)%>% select('subjectcode', 'condition', 'age', 'education', 'gender')

#Test for differences between groups 
  #Age
  df_medal_distinct %>% lm(age ~ condition, data=.) %>% summary()
  
  #education
  chisq.test(df_medal_distinct$education, df_medal_distinct$condition)
  
  #gender
  chisq.test(df_medal_distinct$gender, df_medal_distinct$condition)
  
#Create a Graph  
  #age
  boxplot_age <- ggplot(df_medal_distinct, aes(x=condition, y=age, fill=condition)) +
    geom_boxplot() +
    labs(x = 'Condition', y = 'Age', tag = 'C' ) +
    ggtitle('Age per Condition') +
    scale_fill_manual(values = wesanderson::wes_palette(n=3, name='GrandBudapest2' ))  +
    theme_apa() 
  ggplotly(boxplot_age)
    
    #education
    plot_educ <- ggplot(df_medal_distinct, aes(x=condition, fill = education)) +
      geom_bar(position = 'dodge') +
      labs(x='Condition', y= 'Proportion', tag = 'A' ) +
      ggtitle('Education Level per Condition') +
      scale_fill_manual(values = wesanderson::wes_palette(n=3, name='GrandBudapest2' ), labels = c('Low' , 'Middle' , 'High'), name = 'Education')  +
      theme_apa() 
    ggplotly(plot_educ)
      
    #Gender
    plot_gender <- ggplot(df_medal_distinct, aes(x=condition, fill = gender)) +
      geom_bar(position = 'dodge') +
      labs(x = 'Condition', y = 'Proportion', tag = 'B' ) +
      ggtitle('Gender per Condition') +
      scale_fill_manual(values = wesanderson::wes_palette(n=3, name='GrandBudapest2' ), labels = c('Male', 'Female'), 'Gender')  +
      theme_apa() 
    ggplotly(plot_gender)
      
     plot_gender_2 <-  ggstatsplot::ggbarstats(data = df_medal_distinct, x = gender, y = condition) +
        labs(x = 'Condition', y = 'Proportion', tag = 'B' ) +
        ggtitle('Gender per Condition') +
        scale_fill_manual(values = wesanderson::wes_palette(n=3, name='GrandBudapest2' ), labels = c('Male', 'Female'), 'Gender')  +
        theme_apa() 
      ggplotly(plot_gender_2)

ggpubr::ggarrange(plot_educ, plot_gender, boxplot_age, nrow=2, ncol=2)    

    
```
## Positive Mood {-}

```{r}
describe.by(df_medal_clean$pos_mood, group=df_medal_clean$condition) %>% rbindlist() %>% mutate(vars=c("cont", "rem", "dep"))
```

Distribution

```{r}
ggplot(data= df_medal_clean, aes(x=pos_mood)) + geom_histogram()+facet_grid(cols=vars(condition))
``` 

Centred Distribution 
```{r}
ggplot(data= df_medal_clean, aes(x=pos_mood_c)) + geom_histogram()+facet_grid(cols=vars(condition))
```


## Positive Recent Memory {-}

```{r}
describe.by(df_medal_clean$rec_mem_pos_mood, group=df_medal_clean$condition) %>% rbindlist() %>% mutate(vars=c("cont", "rem", "dep"))
```

Distribution
```{r}
ggplot(data= df_medal_clean, aes(x=rec_mem_pos_mood)) + geom_histogram()+facet_grid(cols=vars(condition))
```

Centred Distribution 
```{r}
ggplot(data= df_medal_clean, aes(x=rec_mem_pos_mood_c)) + geom_histogram()+facet_grid(cols=vars(condition))
```


## Positive Remote Memory {-}

```{r}
describe.by(df_medal_clean$rem_mem_pos_mood, group=df_medal_clean$condition) %>% rbindlist() %>% mutate(vars=c("cont", "rem", "dep"))
```

Distribution

```{r}
ggplot(data= df_medal_clean, aes(x=rem_mem_pos_mood)) + geom_histogram()+facet_grid(cols=vars(condition))
```

Centred Distribution 
```{r}
ggplot(data= df_medal_clean, aes(x=rem_mem_pos_mood_c)) + geom_histogram()+facet_grid(cols=vars(condition))
```


## Negative Mood {-}

```{r}
describe.by(df_medal_clean$neg_mood, group=df_medal_clean$condition)%>% rbindlist() %>% mutate(vars=c("cont", "rem", "dep"))

```

Distribution

```{r}
ggplot(data= df_medal_clean, aes(x=neg_mood)) + geom_histogram()+facet_grid(cols=vars(condition))
```
Centred Distribution 
```{r}
ggplot(data= df_medal_clean, aes(x=neg_mood_c)) + geom_histogram()+facet_grid(cols=vars(condition))
ggplot(data= df_medal_clean, aes(x=neg_mood_cs)) + geom_histogram()+facet_grid(cols=vars(condition))
```


## Negative Recent Memory {-}

```{r}
describe.by(df_medal_clean$rec_mem_neg_mood, group=df_medal_clean$condition) %>% rbindlist() %>% mutate(vars=c("cont", "rem", "dep"))
```

Distribution

```{r}
ggplot(data= df_medal_clean, aes(x=rec_mem_neg_mood)) + geom_histogram()+facet_grid(cols=vars(condition))
```
Centred Distribution 
```{r}
ggplot(data= df_medal_clean, aes(x=rec_mem_neg_mood_c)) + geom_histogram()+facet_grid(cols=vars(condition))
```


## Negative Remote Memory {-}

```{r}
describe.by(df_medal_clean$rem_mem_neg_mood, group=df_medal_clean$condition) %>% rbindlist() %>% mutate(vars=c("cont", "rem", "dep"))
```

Distribution

```{r}
ggplot(data= df_medal_clean, aes(x=rem_mem_neg_mood)) + geom_histogram()+facet_grid(cols=vars(condition))
```
Centred Distribution 
```{r}
ggplot(data= df_medal_clean, aes(x=rem_mem_neg_mood_c)) + geom_histogram()+facet_grid(cols=vars(condition))
```


# Main Effects {.tabset}
A simple linear regression analysis was done to look at the differences in positive and negative mood ratings per condition.

## Linear Mixed Models for PA and NA
```{r}
#positive mood 
positive_model = lmer(pos_mood ~ condition + (1|subjectcode), data = df_medal_clean)


#negative mood
negative_model = lmer(neg_mood ~ condition + (1|subjectcode), data = df_medal_clean)



asis_output(tab_model (positive_model, negative_model, 
                      show.se = T, show.df = T, show.aic = T, transform = NULL,  
                      show.stat = T, show.std = T, 
                      title = 'Condition Differences Mood Rating', dv.labels = c('PA Scaled',  'NA Scaled') )$knitr)
```

## Plot {-}
```{r message=FALSE, warning=FALSE}

#create boxplot for negative and positive affect for each group with significance levels 
combine_data = df_medal_clean %>%
  tidyr::pivot_longer(cols=c(pos_mood, neg_mood), names_to = 'mood_type', values_to = 'mood_rating') %>% 
  dplyr::mutate(mood_type = factor(mood_type, levels = c('pos_mood' , 'neg_mood')))

# Plot
lm_plot = ggplot(combine_data, aes(x=condition, y = mood_rating, fill= condition)) + 
  geom_boxplot() +
  labs(x ='Group', y ='Affect (scaled units)' ) +
  scale_x_discrete(labels = c('control' = 'Never Depressed', 'remitted' = 'Remitted', 'depressed' = 'Depressed' )) +
  facet_wrap( ~ mood_type, ncol =3, nrow = 2, labeller  = labeller(mood_type = c('pos_mood' = 'Positive Affect', 'neg_mood'  = 'Negative Affect'))) +
  geom_signif(comparisons = list(c('control', 'remitted'), c('control' , 'depressed'), c('remitted' , 'depressed' )), map_signif_level = T, y_position = c(15, 13, 11)) +
  scale_fill_manual(values = wesanderson::wes_palette(n=3, name='GrandBudapest2' ))  +
  theme_apa() + 
  guides(fill=F)
ggplotly(lm_plot)
ggsave('plot_simple_regression.pdf', dpi = 320, width = 12, height = 8, path = "figures/")
```

## Follow-Up {-}
```{r}
emmeans::emmeans(positive_model, pairwise ~ condition, pbkrtest.limit = 7800, lmerTest.limit=7800)
emmeans::emmeans(negative_model, pairwise ~ condition, pbkrtest.limit = 7800, lmerTest.limit=7800)
```



# Hypothesis 1: Independent Models  

In this section we test the first models from hypothesis 1 as stated in the pre-registraiton. These analyses look at the relationship between recent and remote emotional memory for both positive and negative affect, while also looking at group differences in these relaitonships based on depression status. 

```{r}
# set model families
model_families = c('gauss-link', "gauss-log", 'gauss-inv', 'Gamma-link', 'Gamma-log')
# detect cores for later
ncores = detectCores()-1
```

## Positive Affect {- .tabset}

First we run the positive affect models. For each of the recent and remote models, we check the best fitting model as stated in the pre-registration based on the AIC and the residuals. We then check whether mediation analyses are warranted, and run those. Within each tab below, we present the results and steps taken, as well as post-hoc analyses to examine the directionality of the effects. 

### Recent {- }
```{r}
# Model equation
h1_post_recent_eq = "pos_mood_cs ~ 1 + condition*rec_mem_pos_mood_cs_lag + gender + age + education + (1 + rec_mem_pos_mood_cs_lag|subjectcode)"

# set clusters
cl = makeCluster(ncores)
registerDoParallel(cl)

# run parallel
h1_pos_recent_models = foreach(family = model_families, .combine = 'c', .packages = c('lmerTest'), .errorhandling = "remove") %dopar% {
  fit_all_mods(h1_post_recent_eq, df_medal_clean, family) }
stopCluster(cl)

# print the models into a single table
asis_output( tab_model(h1_pos_recent_models,  dv.labels = names(h1_pos_recent_models), 
                       show.icc = T, show.aic = T, transform = NULL, show.se = T, digits = 3)$knitr)
```

```{r}
summary(h1_pos_recent_models[[2]])
plot_diagnostics(h1_pos_recent_models)
car::vif(h1_pos_recent_models[[2]]) #Vifs were inspected without interaction and deemed OK
```

##### Follow-up {-}

In the follow-up we look at the pairwise differences in the slopes, and the indiivuds
```{r}
emmeans::emtrends(h1_pos_recent_models[[2]], pairwise ~ condition, var='rec_mem_pos_mood_cs_lag', lmerTest.limit = 3500, pbkrtest.limit = 3500 )
```

```{r}
#create separate dataframes 
df_medal_clean_control = df_medal_clean %>% filter(condition == 'control')
df_medal_clean_remitted = df_medal_clean %>% filter(condition == 'remitted')
df_medal_clean_depressed = df_medal_clean %>% filter(condition == 'depressed') 


m1_pos_rec_control <-   lmer(pos_mood_cs ~ rec_mem_pos_mood_cs_lag + gender + age + education 
                     + (1  +  rec_mem_pos_mood_cs_lag|subjectcode), 
                   data=df_medal_clean_control,
                   control = lmerControl(calc.derivs = F, optimizer='bobyqa', optCtrl=list(maxfun=1e6)))

m1_pos_rec_remitted <-   lmer(pos_mood_cs ~ rec_mem_pos_mood_cs_lag + gender + age + education 
                     + (1 + rec_mem_pos_mood_cs_lag|subjectcode), 
                   data=df_medal_clean_remitted,
                   control = lmerControl(calc.derivs = F, optimizer='bobyqa', optCtrl=list(maxfun=1e6)))

m1_pos_rec_depressed <-   lmer(pos_mood_cs ~ rec_mem_pos_mood_cs_lag + gender + age + education 
                     + (1  + rec_mem_pos_mood_cs_lag|subjectcode), 
                   data=df_medal_clean_depressed,
                   control = lmerControl(calc.derivs = F, optimizer='bobyqa', optCtrl=list(maxfun=1e6)))

asis_output(tab_model(m1_pos_rec_control, m1_pos_rec_remitted, m1_pos_rec_depressed, show.se = T, show.aic = T, transform = NULL, dv.labels = c('control', 'remitted', 'depressed'))$knitr) 
```


### Remote {-}
```{r}
# Model equation
h1_pos_remote_eq = "pos_mood_cs ~ 1 +  condition*rem_mem_pos_mood_cs_lag + gender + age + education + (1  + rem_mem_pos_mood_cs_lag|subjectcode)"

# set clusters
cl = makeCluster(ncores)
registerDoParallel(cl)

# run parallel
h1_pos_remote_models = foreach(family = model_families, .combine = 'c', .packages = c('lmerTest'),  .errorhandling = "remove") %dopar% {
  fit_all_mods(h1_pos_remote_eq, df_medal_clean, family) }
stopCluster(cl)

# print the models into a single table
asis_output( tab_model(h1_pos_remote_models, dv.labels = names(h1_pos_remote_models), 
                       show.icc = T, show.aic = T, transform = NULL, show.se = T, digits = 3)$knitr)
```

```{r}
summary(h1_pos_remote_models[[2]])
plot_diagnostics(h1_pos_remote_models)
car::vif(h1_pos_remote_models[[2]]) #VIFs without interactions were fine
```


##### Follow-up {-}
```{r}
emmeans::emtrends(h1_pos_remote_models[[4]], pairwise ~ condition, var='rem_mem_pos_mood_cs_lag' )

```


```{r}

m1_pos_rem_control <- lmer(pos_mood_cs ~ 1 + rem_mem_pos_mood_cs_lag + gender + age + education + (1  + rem_mem_pos_mood_cs_lag|subjectcode), 
                     data=df_medal_clean_control,
                    control = lmerControl(calc.derivs = F, optimizer='bobyqa', optCtrl=list(maxfun=1e6)))

m1_pos_rem_remitted <- lmer(pos_mood_cs ~ 1 + rem_mem_pos_mood_cs_lag + gender + age + education + (1  + rem_mem_pos_mood_cs_lag|subjectcode), 
                     data=df_medal_clean_remitted,
                     control = lmerControl(calc.derivs = F, optimizer='bobyqa', optCtrl=list(maxfun=1e6)))

m1_pos_rem_depressed <- lmer(pos_mood_cs ~ 1 + rem_mem_pos_mood_cs_lag + gender + age + education + (1  + rem_mem_pos_mood_cs_lag|subjectcode), 
                       data=df_medal_clean_depressed,
                       control = lmerControl(calc.derivs = F, optimizer='bobyqa', optCtrl=list(maxfun=1e6)))

asis_output(tab_model(m1_pos_rem_control, m1_pos_rem_remitted, m1_pos_rem_depressed, show.se = T, show.aic = T, transform = NULL, dv.labels = c('control', 'remitted', 'depressed'))$knitr) 

```




## Negative Affect {- .tabset}

We next run the negative affect models. For each of the recent and remote models, we check the best fitting model as stated in the pre-registration based on the AIC and the residuals. We then check whether mediation analyses are warranted, and run those. Within each tab below, we present the results and steps taken, as well as post-hoc analyses to examine the directionality of the effects

### Recent {-}
```{r}
# Model equation
h1_neg_recent_eq = "neg_mood_cs ~ condition*rec_mem_neg_mood_cs_lag  + gender + age + education + (1 + rec_mem_neg_mood_cs_lag | subjectcode)"
# set clusters
cl = makeCluster(ncores)
registerDoParallel(cl)

# run parallel
h1_neg_recent_models = foreach(family = model_families, .combine = 'c', .packages = c('lmerTest'), .errorhandling = 'remove') %dopar% {
  fit_all_mods(h1_neg_recent_eq, df_medal_clean, family) }
stopCluster(cl)

# print the models into a single table
asis_output( tab_model(h1_neg_recent_models,  dv.labels = names(h1_neg_recent_models), 
                       show.icc = T, show.aic = T, transform = NULL, show.se = T, digits = 3)$knitr)

```


```{r}
summary(h1_neg_recent_models[[4]])
plot_diagnostics(h1_neg_recent_models)
car::vif(h1_neg_recent_models[[4]])
```



#### Follow-up {-}
```{r}
emmeans::emtrends(h1_neg_recent_models[[4]], pairwise ~ condition, var='rec_mem_neg_mood_cs_lag' )

emmeans::emmeans(h1_neg_recent_models[[4]], identity ~ rec_mem_neg_mood_cs_lag | condition )
```


```{r}

m1_neg_rec_control <- glmer(neg_mood_cs ~ 1 + rec_mem_neg_mood_cs_lag + gender + age + education + (0  + rec_mem_neg_mood_cs_lag|subjectcode), 
                   data=df_medal_clean_control,
                   family = Gamma(link=identity),
                   control = glmerControl(calc.derivs = F, optimizer='bobyqa', optCtrl=list(maxfun=1e6)))

m1_neg_rec_remitted <-glmer(neg_mood_cs ~ 1 + rec_mem_neg_mood_cs_lag + gender + age + education + (0  + rec_mem_neg_mood_cs_lag|subjectcode), 
                   data=df_medal_clean_remitted,
                   family = Gamma(link=identity),
                   control = glmerControl(calc.derivs = F, optimizer='bobyqa', optCtrl=list(maxfun=1e6)))

m1_neg_rec_depressed <-glmer(neg_mood_cs ~ 1 + rec_mem_neg_mood_cs_lag + gender + age + education + (0  + rec_mem_neg_mood_cs_lag|subjectcode), 
                   data=df_medal_clean_depressed,
                   family = Gamma(link=identity),
                   control = glmerControl(calc.derivs = F, optimizer='bobyqa', optCtrl=list(maxfun=1e6)))

asis_output(tab_model(m1_neg_rec_control, m1_neg_rec_remitted, m1_neg_rec_depressed, show.se = T, show.aic = T, transform = NULL, dv.labels = c('control', 'remitted', 'depressed'))$knitr) 
```

### Remote {-}
```{r}
# Model equation
h1_neg_remote_eq = "neg_mood_cs ~ condition*rem_mem_neg_mood_cs_lag  + gender + age + education + (0  + rem_mem_neg_mood_cs_lag|subjectcode)"

# set clusters
cl = makeCluster(ncores)
registerDoParallel(cl)

# run parallel
h1_neg_remote_models = foreach(family = model_families, .combine = 'c', .packages = c('lmerTest'),  .errorhandling = "remove") %dopar% {
  fit_all_mods(h1_neg_remote_eq, df_medal_clean, family) }
stopCluster(cl)

# print the models into a single table
asis_output( tab_model(h1_neg_remote_models,   dv.labels = names(h1_neg_remote_models), 
                       show.icc = T, show.aic = T, transform = NULL, show.se = T, digits = 3)$knitr)

```


```{r}
summary(h1_neg_remote_models[[2]])
plot_diagnostics(h1_neg_remote_models)
car::vif(h1_neg_remote_models[[2]])
```


#### Follow-up {-}
```{r}
emmeans::emtrends(h1_neg_remote_models[[2]], pairwise ~ condition, var='rem_mem_neg_mood_cs_lag' )

```

```{r}

m1_neg_rem_control <-  glmer(neg_mood_cs ~ 1 + rem_mem_neg_mood_cs_lag + gender + age + education + (0  + rem_mem_neg_mood_cs_lag|subjectcode), 
                   data=df_medal_clean_control,
                   family = Gamma(link=identity),
                   control = glmerControl(calc.derivs = F, optimizer='bobyqa', optCtrl=list(maxfun=1e6)))

m1_neg_rem_remitted <- glmer(neg_mood_cs ~ 1 + rem_mem_neg_mood_cs_lag + gender + age + education + (0  + rem_mem_neg_mood_cs_lag|subjectcode),
                   data=df_medal_clean_remitted,
                   family = Gamma(link=identity),
                   control = glmerControl(calc.derivs = F, optimizer='bobyqa', optCtrl=list(maxfun=1e6)))

m1_neg_rem_depressed <-  glmer(neg_mood_cs ~ 1 + rem_mem_neg_mood_cs_lag + gender + age + education + (0  + rem_mem_neg_mood_cs_lag|subjectcode),
                   data=df_medal_clean_depressed,
                   family = Gamma(link=identity),
                   control = glmerControl(calc.derivs = F, optimizer='bobyqa', optCtrl=list(maxfun=1e6)))

asis_output(tab_model(m1_neg_rem_control, m1_neg_rem_remitted, m1_neg_rem_depressed, show.se = T, show.aic = T, transform = NULL, dv.labels = c('control', 'remitted', 'depressed'))$knitr) 
```



# Hypothesis 2: Current Affect Moderation 

For H2, we want to see whether current affective states moderate the ability to recall previous affective states. That is, do current negative affect feelings moderate the relationship between affect and recall? 

```{r}
#create separate dataframes for each group
df_medal_clean_control = df_medal_clean %>% filter(condition=='control')
df_medal_clean_remitted = df_medal_clean %>% filter(condition=='remitted')
df_medal_clean_depressed = df_medal_clean %>% filter(condition=='depressed')

#create a new dataframe for the remote variables, where the participants with the singular datapoints are removed 
df_medal_clean_remote = df_medal_clean %>% filter (!(subjectcode == 822 | subjectcode == 906 | subjectcode == 717))

#create separate dataframes per condition 
df_medal_clean_remote_control = df_medal_clean_remote %>% filter(condition=='control')
df_medal_clean_remote_remitted = df_medal_clean_remote %>% filter(condition=='remitted')
df_medal_clean_remote_depressed = df_medal_clean_remote %>% filter(condition=='depressed')
```

## Positive Affect {- .tabset}

### Recent {-}
An lmer model was fitted with gaussian identity, log & inverse as well as Gamma log, inverse, and identity. The models, however, were not a good fit and the gaussian identity model coped with singularity. Hence, a normal linear regression model was fitted. 
```{r fig.height=20, fig.width=15}
h2_pos_recent_eq = "pos_mood_cs ~ condition*rec_mem_pos_mood_cs_lag*pos_mood_cs_lag_rec + gender + age + education + (1 + rec_mem_pos_mood_cs_lag + pos_mood_cs_lag_rec|subjectcode)"

# set clusters
cl = makeCluster(ncores)
registerDoParallel(cl)

# run parallel
h2_pos_recent_models = foreach(family = model_families, .combine = 'c', .packages = c('lmerTest'),  .errorhandling = "remove") %dopar% {
  fit_all_mods(h2_pos_recent_eq, df_medal_clean, family) }
stopCluster(cl)

# print the models into a single table
asis_output( tab_model(h2_pos_recent_models,  dv.labels = names(h2_pos_recent_models), 
                       show.icc = T, show.aic = T, transform = NULL, show.se = T, digits = 3)$knitr)


```

Due to singularity this was determined to be the best fit for the moddel

```{r}
summary(h2_pos_recent_models[[2]])
plot_diagnostics(h2_pos_recent_models)
car::vif(h2_pos_recent_models[[2]])
```



#### Follow-up {-}
```{r}
m2_pos_rec_control <- lm(pos_mood_cs ~ 1 + rec_mem_pos_mood_cs_lag*pos_mood_cs_lag_rec + gender + age + education,
                   data=df_medal_clean_control)

m2_pos_rec_remitted <- lm(pos_mood_cs ~ 1 + rec_mem_pos_mood_cs_lag*pos_mood_cs_lag_rec + gender + age + education,
                   data=df_medal_clean_remitted)

m2_pos_rec_depressed <-  lm(pos_mood_cs ~ 1 + rec_mem_pos_mood_cs_lag*pos_mood_cs_lag_rec + gender + age + education,
                   data=df_medal_clean_depressed)

asis_output(tab_model(m2_pos_rec_control, m2_pos_rec_remitted, m2_pos_rec_depressed, show.se = T, show.aic = T, transform = NULL, dv.labels = c('control', 'remitted', 'depressed'))$knitr)
```


### Remote {- .tabset}

An lmer model was fitted with gaussian identity, log & inverse as well as Gamma log, inverse, and identity. The models, however, were not a good fit and the gaussian identity model coped with singularity. Hence, a normal linear regression model was fitted. 
```{r}
h2_pos_remote_eq = "pos_mood_cs ~ condition*rem_mem_pos_mood_cs_lag*pos_mood_cs_lag_rem + gender + age + education + (1 | subjectcode)"

# set clusters
cl = makeCluster(ncores)
registerDoParallel(cl)

# run parallel
h2_pos_remote_models = foreach(family = model_families, .combine = 'c', .packages = c('lmerTest'),  .errorhandling = "remove") %dopar% {
  fit_all_mods(h2_pos_remote_eq, df_medal_clean, family) }
stopCluster(cl)

# print the models into a single table
asis_output( tab_model(h2_pos_remote_models,  dv.labels = names(h2_pos_remote_models), 
                       show.icc = T, show.aic = T, transform = NULL, show.se = T, digits = 3)$knitr)


```


Due to singularity a regular linear model was fitted.
```{r}
summary(h2_pos_remote_models[[2]])
plot_diagnostics(h2_pos_remote_models)
car::vif(h2_pos_remote_models[[2]])

```


#### Follow-up {-}
```{r}

m2_pos_rem_control <- lm(pos_mood_cs ~ rem_mem_pos_mood_cs_lag*pos_mood_cs_lag_rem  + gender + age + education, 
                   data=df_medal_clean_remote_control)

m2_pos_rem_remitted <- lm(pos_mood_cs ~ rem_mem_pos_mood_cs_lag*pos_mood_cs_lag_rem  + gender + age + education,  
                   data=df_medal_clean_remote_remitted)

m2_pos_rem_depressed <-  lm(pos_mood_cs ~ rem_mem_pos_mood_cs_lag*pos_mood_cs_lag_rem  + gender + age + education, 
                   data=df_medal_clean_remote_depressed)

asis_output(tab_model(m2_pos_rem_control, m2_pos_rem_remitted, m2_pos_rem_depressed, show.se = T, show.aic = T, transform = NULL, dv.labels = c('control', 'remitted', 'depressed'))$knitr) 
```



## Negative Affect {- .tabset}

###  Recent {-}
```{r}
h2_neg_recent_eq = "neg_mood_cs ~ condition*rec_mem_neg_mood_cs_lag*neg_mood_cs_lag_rec + gender + age + education + (1 + rec_mem_neg_mood_cs_lag + neg_mood_cs_lag_rec|subjectcode)"

# set clusters
cl = makeCluster(ncores)
registerDoParallel(cl)

# run parallel
h2_neg_recent_models = foreach(family = model_families, .combine = 'c', .packages = c('lmerTest'),  .errorhandling = "remove") %dopar% {
  fit_all_mods(h2_neg_recent_eq, df_medal_clean, family) }
stopCluster(cl)

# print the models into a single table
asis_output( tab_model(h2_neg_recent_models, dv.labels = names(h2_neg_recent_models), 
                       show.icc = T, show.aic = T, transform = NULL, show.se = T, digits = 3)$knitr)

```

Check Final Model
```{r }
summary(h2_neg_recent_models[[4]])
plot_diagnostics(h2_neg_recent_models) 
car::vif(h2_neg_recent_models[[4]])

```




#### Follow-up {-}
```{r}

m2_neg_rec_control <- glmer(neg_mood_cs ~ rec_mem_neg_mood_cs_lag*neg_mood_cs_lag_rec + 
                              gender + age + education + 
                              (1  + rec_mem_neg_mood_cs_lag | subjectcode) + (1 +  neg_mood_cs_lag_rec|subjectcode), 
                   data=df_medal_clean_control,
                   family = Gamma(link=identity), 
                   control = glmerControl(calc.derivs = F, optimizer='bobyqa', optCtrl=list(maxfun=1e6)))

m2_neg_rec_remitted <- glmer(neg_mood_cs ~ rec_mem_neg_mood_cs_lag*neg_mood_cs_lag_rec + gender + age + education + 
                              (1  + rec_mem_neg_mood_cs_lag | subjectcode) + (1 +  neg_mood_cs_lag_rec|subjectcode), 
                   data=df_medal_clean_remitted,
                   family = Gamma(link=identity), 
                   control = glmerControl(calc.derivs = F, optimizer='bobyqa', optCtrl=list(maxfun=1e6)))

m2_neg_rec_depressed <-  glmer(neg_mood_cs ~ rec_mem_neg_mood_cs_lag*neg_mood_cs_lag_rec + gender + age + education + 
                              (1  + rec_mem_neg_mood_cs_lag | subjectcode) + (1 +  neg_mood_cs_lag_rec|subjectcode),  
                   data=df_medal_clean_depressed,
                   family = Gamma(link=identity), 
                   control = glmerControl(calc.derivs = F, optimizer='bobyqa', optCtrl=list(maxfun=1e6)))

asis_output(tab_model(m2_neg_rec_control, m2_neg_rec_remitted, m2_neg_rec_depressed, show.se = T, show.aic = T, transform = NULL, dv.labels = c('control', 'remitted', 'depressed'))$knitr) 
```




### Remote {-}
```{r}
h2_neg_remote_eq = "neg_mood_cs ~ condition*rem_mem_neg_mood_cs_lag*neg_mood_cs_lag_rem + gender + age + education + (1 + rem_mem_neg_mood_cs_lag + neg_mood_cs_lag_rem|subjectcode)"

# set clusters
cl = makeCluster(ncores)
registerDoParallel(cl)

# run parallel
h2_neg_remote_models = foreach(family = model_families, .combine = 'c', .packages = c('lmerTest'),  .errorhandling = "remove") %dopar% {
  fit_all_mods(h2_neg_remote_eq, df_medal_clean, family) }
stopCluster(cl)

# print the models into a single table
asis_output( tab_model(h2_neg_remote_models, dv.labels = names(h2_neg_remote_models), 
                       show.icc = T, show.aic = T, transform = NULL, show.se = T, digits = 3)$knitr)

```

Check Final Model
```{r}
summary(h2_neg_remote_models[[4]])
plot_diagnostics(h2_neg_remote_models)
car::vif(h2_neg_remote_models[[4]])
```




#### Follow-up {-}
```{r}

m2_neg_rem_control <- glmer(neg_mood_cs ~ rem_mem_neg_mood_cs_lag*neg_mood_cs_lag_rem  + gender + age + education
                            + (1  +  rem_mem_neg_mood_cs_lag|subjectcode) + (1 + neg_mood_cs_lag_rem|subjectcode), 
                   data=df_medal_clean_remote_control,
                   family = Gamma(link=identity), 
                   control = glmerControl(calc.derivs = F, optimizer='bobyqa', optCtrl=list(maxfun=1e6)))

m2_neg_rem_remitted <- glmer(neg_mood_cs ~ rem_mem_neg_mood_cs_lag*neg_mood_cs_lag_rem  + gender + age + education
                            + (1  +  rem_mem_neg_mood_cs_lag|subjectcode) + (1 + neg_mood_cs_lag_rem|subjectcode),
                   data=df_medal_clean_remote_remitted,
                   family = Gamma(link=identity), 
                   control = glmerControl(calc.derivs = F, optimizer='bobyqa', optCtrl=list(maxfun=1e6)))

m2_neg_rem_depressed <-  glmer(neg_mood_cs ~ rem_mem_neg_mood_cs_lag*neg_mood_cs_lag_rem  + gender + age + education
                            + (1  +  rem_mem_neg_mood_cs_lag|subjectcode) + (1 + neg_mood_cs_lag_rem|subjectcode), 
                   data=df_medal_clean_remote_depressed,
                   family = Gamma(link=identity), 
                   control = glmerControl(calc.derivs = F, optimizer='bobyqa', optCtrl=list(maxfun=1e6)))

asis_output(tab_model(m2_neg_rem_control, m2_neg_rem_remitted, m2_neg_rem_depressed, show.se = T, show.aic = T, transform = NULL, dv.labels = c('control', 'remitted', 'depressed'))$knitr) 
```


# Exploratory Analyses

## Mediation Models {.tabset}

Following the tests we ran for hypothesis 1, we would also like to see whether or not mediation effects exist. 

### RecPA {-}

```{r}

#create a new dataframe for the remote variables, where the participants with the singular datapoints are removed 
df_medal_moderation =  df_medal_clean %>% 
  filter (!(subjectcode == 822 | subjectcode == 906 | subjectcode == 717)) %>% 
  filter(!(is.na(pos_mood) | is.na(rec_mem_pos_mood_lag))) 

# med model
med_fit_h1_pos_rec <- lme4::lmer(rec_mem_pos_mood_lag ~ condition_dummy +
                                   age + gender + education + 
                                   (1 |subjectcode),
                                 data = df_medal_moderation)
# full model
out_fit_h1_pos_rec <-lme4::lmer(pos_mood ~ condition_dummy + rec_mem_pos_mood_lag +
                                  age + gender +education +
                                  (1 + rec_mem_pos_mood_lag |subjectcode), 
                                data = df_medal_moderation)

#mediation analysis for control and depressed
med_pos_rec_dep <- mediation::mediate(med_fit_h1_pos_rec, out_fit_h1_pos_rec,
                                      treat = 'condition_dummy' , control.value = 1, treat.value = 3 ,  mediator = 'rec_mem_pos_mood_lag', 
                                      sims=10000)
summary(med_pos_rec_dep)

```


### RemNA {-}
```{r}
df_medal_moderation = df_medal_clean_remote %>% filter(!(is.na(neg_mood) | is.na(rec_mem_neg_mood_lag))) 

# med model
med_fit_h1_neg_rec <- lme4::lmer(rec_mem_neg_mood_lag ~ condition_dummy +
                                   age + gender + education +  
                                   (1 |subjectcode), 
                                 data = df_medal_moderation)
# full model
out_fit_h1_neg_rec <- lme4::lmer(neg_mood ~ condition_dummy + rec_mem_neg_mood_lag +
                                  age + gender +education + 
                                  (1 + rec_mem_neg_mood_lag|subjectcode), 
                                data = df_medal_moderation)


#mediation analysis for control and remitted
summary(mediation::mediate(med_fit_h1_neg_rec, out_fit_h1_neg_rec, 
                           treat = 'condition_dummy' , mediator = 'rec_mem_neg_mood_lag', control.value = 1, treat.value = 2 ,   
                           sims=10000))

#mediation analysis for control and depressed
summary(mediation::mediate(med_fit_h1_neg_rec, out_fit_h1_neg_rec, 
                           treat = 'condition_dummy' ,  mediator = 'rec_mem_neg_mood_lag', control.value = 1, treat.value = 3 ,
                           sims=10000))

```

## Moderated Mediation {.tabset}

### RemPA-CurrPA {-}
```{r}

df_medal_moderation = df_medal_clean_remote %>% 
  filter(!(is.na(pos_mood) | is.na(rem_mem_pos_mood_lag))) 

#mediation first
med_fit_h2_pos_rem <- lme4::lmer(rem_mem_pos_mood_lag ~ condition_dummy + 
                                   age + gender + education + 
                                   (1 |subjectcode), 
                                 data =df_medal_moderation)
# full model
out_fit_h2_pos_rem <-lme4::lmer(pos_mood ~ condition_dummy + rem_mem_pos_mood_lag 
                                + age + gender +education +
                                  (1 + rem_mem_pos_mood_lag |subjectcode),
                                data=df_medal_moderation)


#mediation analysis for control and remitted
med_pos_rem_rem <- mediation::mediate(med_fit_h2_pos_rem, out_fit_h2_pos_rem, 
                                      treat = 'condition_dummy' , control.value = 1, treat.value = 2 ,  mediator = 'rem_mem_pos_mood_lag')
summary(med_pos_rem_rem)

#create new dataframes with only control remitted and only control depressed
df_medal_con_rem = df_medal_clean %>% filter(condition=='control' | condition == 'remitted')
df_medal_con_dep = df_medal_clean %>% filter(condition=='control' | condition == 'depressed')

#remitted group
df_medal_con_rem$condition_dummy = build_contrast(df_medal_con_rem$condition, 'control', 'remitted')
med_h2_pos_rem_rem <- mdt_moderated(data = df_medal_con_rem, IV =condition_dummy, DV = pos_mood, M = rem_mem_pos_mood_lag, Mod = pos_mood_lag_rem) 
rbindlist(med_h2_pos_rem_rem$paths, idcol = T)

```

### RemNA - CurrNA {-}
```{r paged.print=TRUE}
df_medal_moderation = df_medal_clean_remote %>% filter(!(is.na(neg_mood) | is.na(rem_mem_neg_mood_lag))) 

#mediation first
med_fit_h2_neg_rem <- lme4::lmer(rem_mem_neg_mood_lag ~ condition_dummy +
                                   age + gender + education +
                                   (1 |subjectcode),
                                 control = lmerControl(calc.derivs = F, optimizer='bobyqa', optCtrl=list(maxfun=1e6)),
                                 data =df_medal_moderation)

out_fit_h2_neg_rem <-lme4::lmer(neg_mood ~ condition_dummy + rem_mem_neg_mood_lag +
                                  age + gender + education +
                                  (1 + rem_mem_neg_mood_lag |subjectcode), 
                                control = lmerControl(calc.derivs = F, optimizer='bobyqa', optCtrl=list(maxfun=1e6)),
                                data=df_medal_moderation)

#mediation analysis for control and depressed
med_neg_rem_dep <- mediation::mediate(med_fit_h2_neg_rem, out_fit_h2_neg_rem,  
                                      mediator = 'rem_mem_neg_mood_lag',  treat = 'condition_dummy' , 
                                      control.value = 1, treat.value = 3 , sims=10000) 
summary(med_neg_rem_dep)

#mediation analysis for control and remitted
med_neg_rem_rem <- mediation::mediate(med_fit_h2_neg_rem, out_fit_h2_neg_rem, treat = 'condition_dummy' , control.value = 1, treat.value = 2 ,  mediator = 'rem_mem_neg_mood_lag', sims=10000) 
summary(med_neg_rem_rem)

#create new dataframes with only control remitted and only control depressed
df_medal_con_rem = df_medal_clean %>% filter(condition=='control' | condition == 'remitted')
df_medal_con_dep = df_medal_clean %>% filter(condition=='control' | condition == 'depressed')

#remitted group
df_medal_con_rem$condition_dummy = build_contrast(df_medal_con_rem$condition, 'control', 'remitted')
med_h2_neg_rem_rem <- mdt_moderated(data = df_medal_con_rem, IV = condition_dummy, DV = neg_mood, M = rem_mem_neg_mood_lag, Mod = neg_mood_lag_rem) 
rbindlist(med_h2_neg_rem_rem$paths, idcol = T)

#depressed group
df_medal_con_dep$condition_dummy = build_contrast(df_medal_con_dep$condition, 'control', 'depressed')
med_h2_neg_rem_dep <- mdt_moderated(data = df_medal_con_dep, IV = condition_dummy, DV = neg_mood, M = rem_mem_neg_mood_lag, Mod = neg_mood_lag_rem) 
rbindlist(med_h2_neg_rem_dep$paths,idcol = T)
  
```

## Context

```{r}
asis_output(tab_model( (lmer(pos_mood_cs ~ rec_mem_pos_mood_cs_lag*context_location + (1  | subjectcode), data = df_medal_clean)), 
           (lmer(pos_mood_cs ~ rem_mem_pos_mood_cs_lag*context_location + (1  | subjectcode), data = df_medal_clean)),
            (lmer(neg_mood_cs ~ rem_mem_neg_mood_cs_lag*context_location + (1  | subjectcode), data = df_medal_clean)),
             (lmer(neg_mood_cs ~ rem_mem_neg_mood_cs_lag*context_location + (1  | subjectcode), data = df_medal_clean)))$knitr)

```

## SRET

In addition to the EMA, participants also completed the SRET. This is a lab based memory bias measure. We want to check whether our memory bias measures from real-life are linked to ones from the lab in this supplementary analysis. 

```{r}

library('haven')
sret_file = file.path("Z:/", "inbox", "transfer-2023-12-07-02-15-pm", 'MEDAL_pre and post quest and remote recall_workfile!!_for paper only var.sav')
sret_data = read_sav(sret_file) 
sret_data = sret_data %>% select(c(1,4)) %>% distinct()

df_medal_rec_cor1 = df_medal_clean %>%
  dplyr::select(subjectcode, pos_mood, rec_mem_pos_mood_lag) %>%
  na.omit() %>% 
  dplyr::group_by(subjectcode) %>%
  dplyr::mutate(corr =  tryCatch(cor(rec_mem_pos_mood_lag, pos_mood), warning = function(e) NA),
                model_type = "PA_REC") %>% 
  dplyr::select(subjectcode, corr, model_type) %>% 
  dplyr::distinct()  %>% ungroup()


df_medal_rec_cor2 = df_medal_clean %>%
  dplyr::select(subjectcode, neg_mood, rec_mem_neg_mood_lag) %>%
  na.omit() %>% 
  dplyr::group_by(subjectcode) %>%
  dplyr::mutate(corr =  tryCatch(cor(rec_mem_neg_mood_lag, neg_mood), warning = function(e) NA),
                model_type = "NA_REC") %>% 
  dplyr::select(subjectcode, corr, model_type) %>% 
  dplyr::distinct() %>% ungroup()


df_medal_rem_cor1 = df_medal_clean %>%
  dplyr::select(subjectcode, pos_mood, rem_mem_pos_mood_lag) %>%
  na.omit() %>% 
  dplyr::group_by(subjectcode) %>%
  dplyr::mutate(corr = tryCatch(cor(rem_mem_pos_mood_lag, pos_mood), warning = function(e) NA),
                model_type = "PA_REM") %>% 
  dplyr::select(subjectcode, corr, model_type) %>% 
  dplyr::distinct()

df_medal_rem_cor2 = df_medal_clean %>%
  dplyr::select(subjectcode, neg_mood, rem_mem_neg_mood_lag) %>%
  na.omit() %>% 
  dplyr::group_by(subjectcode) %>%
  dplyr::mutate(corr = tryCatch(cor(rem_mem_neg_mood_lag, neg_mood), warning = function(e) NA),
                model_type = "NA_REM") %>% 
  dplyr::select(subjectcode, corr, model_type) %>% 
  dplyr::distinct()


df_cors = rbindlist(list(df_medal_rec_cor1, df_medal_rec_cor2, df_medal_rem_cor1, df_medal_rem_cor2))

df_ref = merge(sret_data, df_cors, by.x = 'subject', by.y = 'subjectcode')
df_ref = df_ref %>% rename(SRET = SRET_BiasGotlib_neg) %>% dplyr::mutate(group = ifelse(subject<800, "remitted", 
                                                                    ifelse(subject>=900, "control", 'depressed')) )


for (mod_type in c("PA_REC", "PA_REM", "NA_REC", "NA_REM")){
  print(mod_type)
  print(summary(lm(SRET ~ corr*group, data = df_ref %>% filter(model_type == mod_type))))
}


```


## Relative Bias

Here we want to check for the relative NA/PA bias. So we extact the random effects from the models to get the slopes for each subject, and then compare the NA/PA slopes. This tells us if theres a bias between negative and positive memory.

```{r}

# Get individual slopes
pos_rec_ref = as.data.frame(ranef(h1_pos_recent_models[[2]])) %>% filter(term!='(Intercept)') %>% select(c(3:5))
pos_rem_ref = as.data.frame(ranef(h1_pos_remote_models[[2]])) %>% filter(term!='(Intercept)') %>% select(c(3:5))
neg_rec_ref = as.data.frame(ranef(h1_neg_recent_models[[4]])) %>% filter(term!='(Intercept)') %>% select(c(3:5))
neg_rem_ref = as.data.frame(ranef(h1_neg_remote_models[[4]])) %>% filter(term!='(Intercept)') %>% select(c(3:5))
pos_rec_ref$mod = "PA_REC"
pos_rem_ref$mod = "PA_REM"
neg_rec_ref$mod = "NA_REC"
neg_rem_ref$mod = "NA_REM"

# Put together into a new data frame
df_ref = rbindlist(list(pos_rec_ref, pos_rem_ref, neg_rec_ref, neg_rem_ref))
df_ref = df_ref %>% 
  dplyr::mutate(sub = as.numeric(as.character(grp))) %>%
  dplyr::mutate(group = ifelse(sub <= 800, "remitted", 
                               ifelse(sub>=900, "control", 'depressed')) )

df_ref_rec = df_ref %>% filter(mod == 'PA_REC' | mod=='NA_REC')
df_ref_rem = df_ref %>% filter(mod == 'PA_REM' | mod=='NA_REM')
summary(lmer(condsd ~ mod*group + (1|sub), data = df_ref_rec))
summary(lmer(condsd ~ mod*group + (1|sub), data = df_ref_rem))
```



# Plots

First we set our theme and fix the data for the figures we want
```{r}
# Theme
ggtheme = theme(text = element_text(size = 8), 
                panel.background = element_rect(fill = "transparent"), 
                panel.grid.major = element_line(color = "grey90"),
                panel.grid.minor = element_line(color = "grey100"),
                panel.border = element_rect(color = "grey80", fill = "transparent"))

# Estimate the SD
df_medal_clean2 = df_medal_clean %>% 
  mutate(pos_mood_sd = ifelse( (pos_mood_cs_lag_rec > (mean(pos_mood_cs_lag_rec, na.rm=T)+sd(pos_mood_cs_lag_rec, na.rm=T))), "+1SD",
                               ifelse( (pos_mood_cs_lag_rec < (mean(pos_mood_cs_lag_rec, na.rm=T)-sd(pos_mood_cs_lag_rec, na.rm =T))), "-1SD", "Mean"))) %>% 
    mutate(neg_mood_sd = ifelse( (neg_mood_cs_lag_rec > (mean(neg_mood_cs_lag_rec, na.rm=T)+sd(neg_mood_cs_lag_rec, na.rm=T))), "+1SD",
                               ifelse( (neg_mood_cs_lag_rec < (mean(neg_mood_cs_lag_rec, na.rm=T)-sd(neg_mood_cs_lag_rec, na.rm =T))), "-1SD", "Mean")))

# Fix factor levels
df_medal_clean2$Condition =  factor(df_medal_clean2$condition, levels=c('control', 'remitted', 'depressed' ), labels=c('Never-Depressed', 'Remitted', 'Depressed'))
df_medal_clean2$pos_mood_sd =  factor(df_medal_clean2$pos_mood_sd, levels=c('-1SD', 'Mean', '+1SD' ))
df_medal_clean2$neg_mood_sd =  factor(df_medal_clean2$neg_mood_sd, levels=c('-1SD', 'Mean', '+1SD' ))


```

### PA-CUR-REC
```{r}

# Plot
plot_pa_rec = ggplot(df_medal_clean2, aes(x = rec_mem_pos_mood_c_lag, y=pos_mood_c, color = pos_mood_sd, fill = pos_mood_sd)) +
  geom_smooth(method = 'lm', alpha = 0.15) +
  labs( x = bquote(paste(REC[PA], " (subject-centered a.u.)")), y = "PA (subject-centered a.u.)", color = bquote(CUR[PA]), fill = bquote(CUR[PA]) ) +
  ggtheme + 
  scale_color_manual(values = wesanderson::wes_palette(n=3, name='GrandBudapest2' ))  +
  scale_fill_manual(values = wesanderson::wes_palette(n=3, name='GrandBudapest2' ))  +
  facet_grid(.~Condition) 
 
plot_pa_rec
```

### PA-CUR-REC

```{r}

# Plot
plot_na_rec = 
  ggplot(df_medal_clean2, aes(x = rec_mem_neg_mood_c_lag, y=neg_mood_c, color = neg_mood_sd, fill = neg_mood_sd)) +
  geom_smooth(method = 'lm', alpha = 0.15) +
  labs( x = bquote(paste(REC['NA'], " (subject-centered a.u.)")), y = "NA (subject-centered a.u.)", color = bquote(CUR["NA"]), fill = bquote(CUR["NA"]) ) +
    scale_color_manual(values = wesanderson::wes_palette(n=3, name='GrandBudapest2' ))  +
  scale_fill_manual(values = wesanderson::wes_palette(n=3, name='GrandBudapest2' ))  +
  ggtheme + 
  facet_grid(.~Condition) 
plot_na_rec
```

### Combined Plot for pub
```{r}
ggarrange(NA, plot_pa_rec, NA,  plot_na_rec,
          widths=c(0.05, 1, 0.05, 1), ncol = 2, nrow = 2,
          labels=c("A", NA, "B"))
ggsave("figures/fig1_threeway.pdf", device = 'pdf', dpi = 320, height = 6)

```







